# correlation does not imply causation

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Everydata: The Misinformation Hidden in the Little Data You Consume Every Day by John H. Johnson

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This book isn’t meant to be a statistics textbook. Unfortunately, we don’t have the space to teach you how to run a perfect statistical analysis, or determine the exact correlation. But that’s okay, because our goal is simply to help you make better decisions by recognizing the difference between correlation and causation, and understanding some of the reasons that people confuse the ­t wo—​­so you can avoid making the same mistakes. How to Be a Good Consumer of Correlation and Causation So now, armed with a better understanding of the distinction between correlation and causation, here are some steps to keep in mind when consuming data about a statistical relationship: 1. Ask yourself what is being represented in the news article or research. Does the story actually use the phrase “causal” relationship? 221158 i-xiv 1-210 r4ga.indd  62 2/8/16  5:58:50 PM Are You Smarter Than an ­iPhone-­U sing, ­R adiohead-­L oving Republican?

So if you really want a “Proud parent of an honor roll student” bumper sticker for your minivan, apparently all you need to do is get your kids glasses and an iPhone, have them watch a few Ronald Reagan speeches, play some Radiohead, don’t let them fall asleep before midnight, turn them into lefties, and start them drinking (once they reach legal age, of course). Have we lost our minds? No. We’ve just read a lot of studies and media reports that seem to draw the wrong conclusion from statistical ­analyses—​­specifically, reports and articles that confuse correlation with causation, and therefore, sometimes unintentionally, mislead the reader about the key takeaways. It’s important to note that there are two issues here: first of all, there are the original scientific studies that sometimes confuse correlation with causation. But what you’re more likely to encounter in your everyday life are newspaper articles and other media accounts that misreport the findings from valid scientific studies. We’ve seen many cases in which a finding is reported in the news as causation, even though the underlying study notes that it is only correlation.

These types of ­connections—​­when there is some sort of relationship between ­data—​­are called correlations. But, as we’ll explore in this chapter, the mere existence of such a statistical relationship between two factors does not imply that there is actually a meaningful link between them. Correlation does not equal causation. It’s actually one of the most common ways that people misinterpret data. But don’t ­worry—​­in this chapter, we’ll take a close look at how and why people mistake correlation for causation, and give you the tools to help you understand which everydata you should really believe. Smartphones = Smart People? So, back to the smart people analysis. We dug a bit deeper into what the actual studies said, and uncovered some interesting caveats, warnings, and facts that might shed some light on these findings. 221158 i-xiv 1-210 r4ga.indd  46 2/8/16  5:58:50 PM Are You Smarter Than an ­iPhone-­U sing, ­R adiohead-­L oving Republican?

pages: 579 words: 76,657

Data Science from Scratch: First Principles with Python by Joel Grus

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That is the sort of relationship that correlation looks for. In addition, correlation tells you nothing about how large the relationship is. The variables: x = [-2, 1, 0, 1, 2] y = [99.98, 99.99, 100, 100.01, 100.02] are perfectly correlated, but (depending on what you’re measuring) it’s quite possible that this relationship isn’t all that interesting. Correlation and Causation You have probably heard at some point that “correlation is not causation,” most likely by someone looking at data that posed a challenge to parts of his worldview that he was reluctant to question. Nonetheless, this is an important point — if x and y are strongly correlated, that might mean that x causes y, that y causes x, that each causes the other, that some third factor causes both, or it might mean nothing. Consider the relationship between num_friends and daily_minutes.

closeness centrality, Betweenness Centrality clustering, Clustering-For Further Explorationbottom-up hierarchical clustering, Bottom-up Hierarchical Clustering-Bottom-up Hierarchical Clustering choosing k, Choosing k example, clustering colors, Example: Clustering Colors example, meetups, Example: Meetups-Example: Meetups k-means clustering, The Model clusters, Rescaling, The Ideadistance between, Bottom-up Hierarchical Clustering code examples from this book, Using Code Examples coefficient of determination, The Model combiners (in MapReduce), An Aside: Combiners comma-separated values files, Delimited Filescleaning comma-delimited stock prices, Cleaning and Munging command line, running Python scripts at, stdin and stdout conditional probability, Conditional Probabilityrandom variables and, Random Variables confidence intervals, Confidence Intervals confounding variables, Simpson’s Paradox confusion matrix, Correctness continue statement (Python), Control Flow continuity correction, Example: Flipping a Coin continuous distributions, Continuous Distributions control flow (in Python), Control Flow correctness, Correctness correlation, Correlationand causation, Correlation and Causation in simple linear regression, The Model other caveats, Some Other Correlational Caveats outliers and, Correlation Simpson's Paradox and, Simpson’s Paradox correlation function, Simple Linear Regression cosine similarity, User-Based Collaborative Filtering, Item-Based Collaborative Filtering Counter (Python), Counter covariance, Correlation CREATE TABLE statement (SQL), CREATE TABLE and INSERT cumulative distribution function (cdf), Continuous Distributions currying (Python), Functional Tools curse of dimensionality, The Curse of Dimensionality-The Curse of Dimensionality, User-Based Collaborative Filtering D D3.js library, Visualization datacleaning and munging, Cleaning and Munging exploring, Exploring Your Data-Many Dimensions finding, Find Data getting, Getting Data-For Further Explorationreading files, Reading Files-Delimited Files scraping from web pages, Scraping the Web-Example: O’Reilly Books About Data using APIs, Using APIs-Using Twython using stdin and stdout, stdin and stdout manipulating, Manipulating Data-Manipulating Data rescaling, Rescaling-Rescaling data mining, What Is Machine Learning?

pages: 219 words: 65,532

The Numbers Game: The Commonsense Guide to Understanding Numbers in the News, in Politics, and inLife by Michael Blastland; Andrew Dilnot

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During medical trials of new drugs, it used to be customary to record anything that happened to a patient taking an experimental drug and say the drug might have caused it: “side effects,” they were called, as it was noted that someone had a headache or a runny nose and thereafter this “side effect” was printed forever on the side of the packet. Nowadays these are referred to as “adverse events,” making it clear that the cause was unclear and they might have had nothing to do with the medication. Restlessness for the true cause is a constructive habit, an insurance against gullibility. And though correlation does not prove causation, it is often a good hint, but a hint to start asking questions, not to settle for easy answers. There is one caveat. Here and there you will come across a tendency to dismiss almost all statistical findings as correlation-causation fallacy, a rhetorical cudgel, as one careful critic put it, to avoid believing any evidence. But we need to distinguish between casual associations often made for political ends and proper statistical studies. The latter come to their conclusions by trying to eliminate all the other possible causes through careful control of any trial, sample, or experiment, making sure if they can that there is no bias, that samples are random when possible.

Human (and sometimes animal) ability to see how one thing leads to another is prodigious—thank goodness, since it is vital to survival. But it also goes badly wrong. From applying it all the time, people acquire a headstrong tendency to see it everywhere, even where it isn’t. We see how one thing goes with another—and quickly conclude that it causes the other, and never more so than when the numbers or measurements seem to agree. This is the oldest fallacy in the book, that correlation proves causation, and also the most obdurate. And so it has been observed by smart researchers that overweight people live longer than thinner people, and therefore it was concluded that being overweight causes longer life. Does it? We will see. How do we train the instinct that serves us so well most of the time for the occasions when it doesn’t? Not by keeping it in check—it is genius at work—but by refusing to let it sleep.

That is a cheap joke. There are many possible causes of acne, even in lovers of heavy metal, the likelier culprits being teenage hormones and diet. Correlation—the apparent link between two separate things—does not prove causation: just because two things seem to go together doesn’t mean one brings about the other. This shouldn’t need saying, but it does, hourly. Get this wrong—mistake correlation for causation—and we flout one of the most elementary rules of statistics or logic. When we spot a fallacy of this kind lurking behind a claim, we cannot believe anyone could have fallen for it. That is, until tomorrow, when we miss precisely the same kind of fallacy and then see fit to say the claim is supported by compelling evidence. It is frighteningly easy to think in this way. Time and again someone measures a change in A, notes another in B, and declares one the mother of the other.

pages: 227 words: 62,177

Numbers Rule Your World: The Hidden Influence of Probability and Statistics on Everything You Do by Kaiser Fung

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But imperfect information does not intimidate them; they seek models that fit the available evidence more tightly than all alternatives. Box’s writings on his experiences in the industry have inspired generations of statisticians; to get a flavor of his engaging style, see the collection Improving Almost Anything, lovingly produced by his former students. More ink than necessary has been spilled on the dichotomy between correlation and causation. Asking for the umpteenth time whether correlation implies causation is pointless (we already know it does not). The question Can correlation be useful without causation? is much more worthy of exploration. Forgetting what the textbooks say, most practitioners believe the answer is quite often yes. In the case of credit scoring, correlation-based statistical models have been wildly successful even though they do not yield simple explanations for why one customer is a worse credit risk than another.

It is implausible that something as variable as human behavior can be attributed to simple causes; modelers specializing in stock market investment and consumer behavior have also learned similar lessons. Statisticians in these fields have instead relied on accumulated learning from the past. The standard statistics book grinds to a halt when it comes to the topic of correlation versus causation. As readers, we may feel as if the authors have taken us along for the ride! After having plodded through the mathematics of regression modeling, we reach a section that screams, “Correlation is not causation!” and, “Beware of spurious correlations!” over and over. The bottom line, the writers tell us, is that almost nothing we have studied can prove causation; their motley techniques measure only correlation. The greatest statistician of his generation, Sir Ronald Fisher, famously scoffed at Hill’s technique to link cigarette smoking and lung cancer; he offered that the discovery of a gene that predisposes people to both smoking and cancer would discredit such a link.

As interesting as it would be to know how each step of a touring plan decreased their wait times, Testa’s millions of fans care about only one thing: whether the plan let them visit more rides, enhancing the value of their entry tickets. The legion of satisfied readers is testimony to the usefulness of this correlational model. ~###~ Polygraphs rely strictly on correlations between the act of lying and certain physiological metrics. Are correlations useful without causation? In this case, statisticians say no. To avoid falsely imprisoning innocent people based solely on evidence of correlation, they insist that lie detection technology adopt causal modeling of the type practiced in epidemiology. They caution against logical overreach: Liars breathe faster. Adam’s breaths quickened. Therefore, Adam was a liar. Deception, or stress related to it, is only one of many possible causes for the increase in breathing rate, so variations in this or similar measures need not imply lying.

pages: 397 words: 109,631

Mindware: Tools for Smart Thinking by Richard E. Nisbett

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Many of my fellow psychologists are going to be distressed by my bottom line here: such questions as whether academic success is affected by self-esteem, controlling for depression, or whether the popularity of fraternity brothers is affected by extroversion, controlling for neuroticism, or whether the number of hugs a person receives per day confers resistance to infection, controlling for age, educational attainment, frequency of social interaction, and a dozen other variables, are not answerable by MRA. What nature hath joined together, multiple regression analysis cannot put asunder. No Correlation Doesn’t Mean No Causation Correlation doesn’t prove causation. But the problem with correlational studies is worse than that. Lack of correlation doesn’t prove lack of causation—and this mistake is made possibly as often as the converse error. Does diversity training improve rates of hiring women and minorities? One study examined this question by quizzing human resource managers at seven hundred U.S. organizations about whether they had diversity training programs and by checking on the firms’ minority hiring rates filed with the Equal Employment Opportunity Commission.31 As it happens, having diversity training programs was unrelated to “the share of white women, black women, and black men in management.”

The representativeness heuristic underlies many of our prior assumptions about correlation. If A is similar to B in some respect, we’re likely to see a relationship between them. The availability heuristic can also play a role. If the occasions when A is associated with B are more memorable than occasions when it isn’t, we’re particularly likely to overestimate the strength of the relationship. Correlation doesn’t establish causation, but if there’s a plausible reason why A might cause B, we readily assume that correlation does indeed establish causation. A correlation between A and B could be due to A causing B, B causing A, or something else causing both. We too often fail to consider these possibilities. Part of the problem here is that we don’t recognize how easy it is to “explain” correlations in causal terms. Reliability refers to the degree to which a case gets the same score on two occasions or when measured by different means.

A basic problem with MRA is that it typically assumes that the independent variables can be regarded as building blocks, with each variable taken by itself being logically independent of all the others. This is usually not the case, at least for behavioral data. Self-esteem and depression are intrinsically bound up with each other. It’s entirely artificial to ask whether one of those variables has an effect on a dependent variable independent of the effects of the other variable. Just as correlation doesn’t prove causation, absence of correlation fails to prove absence of causation. False-negative findings can occur using MRA just as false-positive findings do—because of the hidden web of causation that we’ve failed to identify. 12. Don’t Ask, Can’t Tell How many questionnaire and survey results about people’s beliefs, values, or behavior will you read during your lifetime in newspapers, magazines, and business reports? Thousands, surely.

pages: 267 words: 71,123

End This Depression Now! by Paul Krugman

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Inequality and Crises Before the financial crisis of 2008 struck, I would often give talks to lay audiences about income inequality, in which I would point out that top income shares had risen to levels not seen since 1929. Invariably there would be questions about whether that meant that we were on the verge of another Great Depression—and I would declare that this wasn’t necessarily so, that there was no reason extreme inequality would necessarily cause economic disaster. Well, whaddya know? Still, correlation is not the same as causation. The fact that a return to pre-Depression levels of inequality was followed by a return to depression economics could be just a coincidence. Or it could reflect common causes of both phenomena. What do we really know here, and what might we suspect? Common causation is almost surely part of the story. There was a major political turn to the right in the United States, the United Kingdom, and to some extent other countries circa 1980.

Before I can answer that question, I have to talk briefly about the pitfalls one needs to avoid. The Trouble with Correlation You might think that the way to assess the effects of government spending on the economy is simply to look at the correlation between spending levels and other things, like growth and employment. The truth is that even people who should know better sometimes fall into the trap of equating correlation with causation (see the discussion of debt and growth in chapter 8). But let me try to disabuse you of the notion that this is a useful procedure, by talking about a related question: the effects of tax rates on economic performance. As you surely know, it’s an article of faith on the American right that low taxes are the key to economic success. But suppose we look at the relationship between taxes—specifically, the share of GDP collected in federal taxes—and unemployment over the past dozen years.

pages: 347 words: 99,969

Through the Language Glass: Why the World Looks Different in Other Languages by Guy Deutscher

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Since the rooms face each other (rather like rooms 1 and 2 in the picture shown here), and since they have been arranged to look the same from the egocentric perspective, they are actually north-side-south. In his room the bed was in the north, in yours it is in the south; the telephone that in his room was in the west is now in the east. So while you will see and remember the same room twice, the Guugu Yimithirr speaker will see and remember two different rooms. CORRELATION OR CAUSATION? One of the most tempting and most common of all logical fallacies is to jump from correlation to causation: to assume that just because two facts correlate, one of them was the cause of the other. To reduce this kind of logic ad absurdum, I could advance the brilliant new theory that language can affect your hair color. In particular, I claim that speaking Swedish makes your hair go blond and speaking Italian makes your hair go dark. My proof?

There is no evidence of formal tuition in geographic coordinates at an early age (although there is evidence from Bali of some geographically relevant religious practices, such as putting children to bed with the head pointing in a particular geographic direction). So the only imaginable mechanism that could provide such intense drilling in orientation at such a young age is the spoken language—the need to know the directions in order to be able to communicate about the simplest aspects of everyday life. There is thus a compelling case that the relation between language and spatial thinking is not just correlation but causation, and that one’s mother tongue affects how one thinks about space. In particular, a language like Guugu Yimithirr, which forces its speakers to use geographic coordinates at all times, must be a crucial factor in bringing about the perfect pitch for directions and the corresponding patterns of memory that seem so weird and unattainable to us. Two centuries after Guugu Yimithirr bequeathed “kangaroo” to the world, its last remaining speakers gave the world a harsh lesson in philosophy and psychology.

And while this is as much as we can say with absolute certainty, it is plausible to go one step further and make the following inference: since people tend to react more quickly to color recognition tasks the farther apart the two colors appear to them, and since Russians react more quickly to shades across the siniy-goluboy border than what the objective distance between the hues would imply, it is plausible to conclude that neighboring hues around the border actually appear farther apart to Russian speakers than they are in objective terms. Of course, even if differences between the behavior of Russian and English speakers have been demonstrated objectively, it is always dangerous to jump automatically from correlation to causation. How can we be sure that the Russian language in particular—rather than anything else in the Russians’ background and upbringing—had any causal role in producing their response to colors near the border? Maybe the real cause of their quicker reaction time lies in the habit of Russians to spend hours on end gazing intently at the vast expanses of Russian sky? Or in years of close study of blue vodka?

pages: 624 words: 127,987

The Personal MBA: A World-Class Business Education in a Single Volume by Josh Kaufman

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Midranges are best used for quick estimates—they’re fast, and you only need to know two data points, but they can be easily skewed by outliers that are abnormally high or low, like Bill Gates’s bank balance. Means, Medians, Modes, and Midranges are useful analytical tools that can indicate typical results—provided you’re careful enough to use the right tool for the job. SHARE THIS CONCEPT: http://book.personalmba.com/mean-median-mode-midrange/ Correlation and Causation Correlation isn’t causation, but it sure is a hint. —EDWARD TUFTE, STATISTICIAN, INFORMATION DESIGN EXPERT, AND PROFESSOR AT YALE UNIVERSITY Imagine a billiards table: if you know the exact position of every ball on the table and the details of the forces applied to the cue ball (impact vector, impact force, location of impact, table friction, and air resistance), you can calculate exactly how the cue ball will travel and how it will affect other balls it hits along the way.

Here’s another thought experiment, using hypothetical data: people who suffer heart attacks eat, on average, 57 bacon double cheeseburgers every year. Does eating bacon double cheeseburgers cause heart attacks? Not necessarily. People who suffer heart attacks typically take 365 showers a year and blink their eyes 5.6 million times a year. Do taking showers and blinking your eyes cause heart attacks as well? Correlation is not Causation. Even if you notice that one measurement is highly associated with another, that does not prove that one thing caused the other. Imagine you own a pizza parlor, and you create a thirty-second advertisement to air on local television. Shortly after the commercial goes live, you notice a 30 percent increase in sales. Did the advertisement cause the increase? Not necessarily—the increase could be due to any number of factors.

For example, if you know that families go out to celebrate the end of school or that an annual convention is coming up, you can adjust for that seasonality by using historical data. The more you can isolate the change you made in the system from other factors, the more confidence you can have that the change you made intentionally actually caused the results you see. SHARE THIS CONCEPT: http://book.personalmba.com/correlation-causation/ Norms Those who cannot remember the past are condemned to repeat it. —GEORGE SANTAYANA, PHILOSOPHER, ESSAYIST, AND APHORIST If you want to compare the effectiveness of something in the present, it’s often useful to learn from the past. Norms are measures that use historical data as a tool to provide Context for current Measurements. For example, by looking at past data you may discover trends in your sales data directly related to the date the sale was made, which is called seasonality.

The Panic Virus: The True Story Behind the Vaccine-Autism Controversy by Seth Mnookin

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Before continuing with the nationwide effort, his aides said, public health officials needed to promise that there would not be any more children who were diagnosed with polio after being vaccinated. That, as anyone with an elementary understanding of immunology knew, was an impossible guarantee to provide, and so, instead of trusting people to understand and accept that there are risks with every medical procedure and that correlation does not equal causation, or trying to explain that the problems appeared to be related to the specific conditions under which the infected batches had been produced and not with the safety of the vaccine generally, the government took the one step guaranteed to undermine public confidence: On May 7, Scheele announced that the polio vaccine program was being shut down so that the government, “with the help of the manufacturers,” could undertake “a reappraisal of all of their tests and procedures.”13 “The Public Health Service believes that every single step in the interest of safety must be taken,” he said.

., which was delivered under the protection of four policemen. Proving that the media’s frenzy for beating competitors by mere minutes is not a product of the Internet age, NBC immediately broke the embargo, and was just as quickly denounced by its competitors as forever tainting the sanctity of agreements made between reporters and their sources. 13 The difficulty in determining whether correlation equals causation causes an enormous number of misapprehensions. Until a specific mechanism demonstrating how A causes B is identified, it’s best to assume that any correlation is incidental, or that both A and B relate independently to some third factor. An example that highlights this is the correlation between drinking milk and cancer rates, which some advocacy groups (including People for the Ethical Treatment of Animals) use to argue that drinking milk causes cancer.

In order to square that circle, Kirby likened the dispute to a political campaign in which an “insurgent candidate” comes under “heavy fire from an entrenched opponent . . . the vitriol demonstrates that the challenge is being taken seriously, that it poses a realistic threat to the status quo.” In this political battle, Kirby employed a time-honored tactic of push pollers and ward politicians: He used an ominoussounding claim—“Curiously, the first case of autism was not recorded until the early 1940s, a few years after thimerosal was introduced in vaccines”—to make his accusation sound as if it was idle speculation. In this case, Kirby both blurred the difference between correlation and causation and conflated the first time a disease is given a particular label with the first time it appears in a population. (It was a little like saying, “Curiously, schizophrenia was not identified as a disorder until the late 1880s, a few years after Alexander Graham Bell invented the telephone.”) He also larded his writing with conditional statements and passive constructions: Eli Lilly “reportedly earn[ed] a profit” by licensing thimerosal to other drug companies; “the American health establishment . . . understandably has an interest in proving the unpleasant [thimerosal] theory wrong.”

pages: 337 words: 86,320

Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are by Seth Stephens-Davidowitz

And the last minute of this game will do something that, for an economist, is far more profound: the last sixty seconds will help finally tell us, once and for all, Do advertisements work? The notion that ads improve sales is obviously crucial to our economy. But it is maddeningly hard to prove. In fact, this is a textbook example of exactly how difficult it is to distinguish between correlation and causation. There’s no doubt that products that advertise the most also have the highest sales. Twentieth Century Fox spent \$150 million marketing the movie Avatar, which became the highest-grossing film of all time. But how much of the \$2.7 billion in Avatar ticket sales was due to the heavy marketing? Part of the reason 20th Century Fox spent so much money on promotion was presumably that they knew they had a desirable product.

If we did this, we would find that students who went to Stuyvesant score much higher on standardized tests and get accepted to substantially better universities. But as we’ve seen already in this chapter, this kind of evidence, by itself, is not convincing. Maybe the reason Stuyvesant students perform so much better is that Stuy attracts much better students in the first place. Correlation here does not prove causation. To test the causal effects of Stuyvesant High School, we need to compare two groups that are almost identical: one that got the Stuy treatment and one that did not. We need a natural experiment. But where can we find it? The answer: students, like Yilmaz, who scored very, very close to the cutoff necessary to attend Stuyvesant.* Students who just missed the cutoff are the control group; students who just made the cut are the treatment group.

Take college. Does it matter if you go to one of the best universities in the world, such as Harvard, or a solid school such as Penn State? Once again, there is a clear correlation between the ranking of one’s school and how much money people make. Ten years into their careers, the average graduate of Harvard makes \$123,000. The average graduate of Penn State makes \$87,800. But this correlation does not imply causation. Two economists, Stacy Dale and Alan B. Krueger, thought of an ingenious way to test the causal role of elite universities on the future earning potential of their graduates. They had a large dataset that tracked a whole host of information on high school students, including where they applied to college, where they were accepted to college, where they attended college, their family background, and their income as adults.

pages: 829 words: 186,976

The Signal and the Noise: Why So Many Predictions Fail-But Some Don't by Nate Silver

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However, in order to achieve that purity, it denies the need for Bayesian priors or any other sort of messy real-world context. These methods neither require nor encourage us to think about the plausibility of our hypothesis: the idea that cigarettes cause lung cancer competes on a level playing field with the idea that toads predict earthquakes. It is, I suppose, to Fisher’s credit that he recognized that correlation does not always imply causation. However, the Fisherian statistical methods do not encourage us to think about which correlations imply causations and which ones do not. It is perhaps no surprise that after a lifetime of thinking this way, Fisher lost the ability to tell the difference. Bob the Bayesian In the Bayesian worldview, prediction is the yardstick by which we measure progress. We can perhaps never know the truth with 100 percent certainty, but making correct predictions is the way to tell if we’re getting closer.

As Hatzius sees it, economic forecasters face three fundamental challenges. First, it is very hard to determine cause and effect from economic statistics alone. Second, the economy is always changing, so explanations of economic behavior that hold in one business cycle may not apply to future ones. And third, as bad as their forecasts have been, the data that economists have to work with isn’t much good either. Correlations Without Causation The government produces data on literally 45,000 economic indicators each year.24 Private data providers track as many as four million statistics.25 The temptation that some economists succumb to is to put all this data into a blender and claim that the resulting gruel is haute cuisine. There have been only eleven recessions since the end of World War II.26 If you have a statistical model that seeks to explain eleven outputs but has to choose from among four million inputs to do so, many of the relationships it identifies are going to be spurious.

But it has given roughly as many false alarms—including most infamously in 1984, when it sharply declined for three straight months,34 signaling a recession, but the economy continued to zoom upward at a 6 percent rate of growth. Some studies have even claimed that the Leading Economic Index has no predictive power at all when applied in real time.35 “There’s very little that’s really predictive,” Hatzius told me. “Figuring out what’s truly causal and what’s correlation is very difficult to do.” Most of you will have heard the maxim “correlation does not imply causation.” Just because two variables have a statistical relationship with each other does not mean that one is responsible for the other. For instance, ice cream sales and forest fires are correlated because both occur more often in the summer heat. But there is no causation; you don’t light a patch of the Montana brush on fire when you buy a pint of Häagen-Dazs. If this concept is easily expressed, however, it can be hard to apply in practice, particularly when it comes to understanding the causal relationships in the economy.

pages: 321 words: 97,661

How to Read a Paper: The Basics of Evidence-Based Medicine by Trisha Greenhalgh

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Whom is the study about? Was the design of the study sensible? Was systematic bias avoided or minimised? Was assessment ‘blind’? Were preliminary statistical questions addressed? Summing up References Chapter 5: Statistics for the non-statistician How can non-statisticians evaluate statistical tests? Have the authors set the scene correctly? Paired data, tails and outliers Correlation, regression and causation Probability and confidence The bottom line Summary References Chapter 6: Papers that report trials of drug treatments and other simple interventions ‘Evidence’ and marketing Making decisions about therapy Surrogate endpoints What information to expect in a paper describing a randomised controlled trial: the CONSORT statement Getting worthwhile evidence out of a pharmaceutical representative References Chapter 7: Papers that report trials of complex interventions Complex interventions Ten questions to ask about a paper describing a complex intervention References Chapter 8: Papers that report diagnostic or screening tests Ten men in the dock Validating diagnostic tests against a gold standard Ten questions to ask about a paper that claims to validate a diagnostic or screening test Likelihood ratios Clinical prediction rules References Chapter 9: Papers that summarise other papers (systematic reviews and meta-analyses) When is a review systematic?

Non-normal (skewed) data can sometimes be transformed to give a normal-shape graph by plotting the logarithm of the skewed variable or performing some other mathematical transformation (such as square root or reciprocal). Some data, however, cannot be transformed into a smooth pattern, and the significance of this is discussed subsequently. Deciding whether data are normally distributed is not an academic exercise, because it will determine what type of statistical tests to use. For example, linear regression (see section ‘Correlation, regression and causation’) will give misleading results unless the points on the scatter graph form a particular distribution about the regression line—that is, the residuals (the perpendicular distance from each point to the line) should themselves be normally distributed. Transforming data to achieve a normal distribution (if this is indeed achievable) is not cheating. It simply ensures that data values are given appropriate emphasis in assessing the overall effect.

I assumed this was a transcription error, so I moved the decimal point two places to the left. Some weeks later, I met the technician who had analysed the specimens and he asked ‘Whatever happened to that chap with acromegaly?’ Statistically correcting for outliers (e.g. to modify their effect on the overall result) is quite a sophisticated statistical manoeuvre. If you are interested, try the relevant section in your favourite statistics textbook. Correlation, regression and causation Has correlation been distinguished from regression, and has the correlation coefficient (‘r-value’) been calculated and interpreted correctly? For many non-statisticians, the terms correlation and regression are synonymous, and refer vaguely to a mental image of a scatter graph with dots sprinkled messily along a diagonal line sprouting from the intercept of the axes. You would be right in assuming that if two things are not correlated, it will be meaningless to attempt a regression.

The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences by Rob Kitchin

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However, while pattern recognition might identify potentially interesting relationships, the veracity of these needs to be further tested on other datasets to ensure their reliability and validity. In other words, the relationships should form the basis for hypotheses that are more widely tested, which in turn are used to build and refine a theory that explains them. Thus correlations do not supersede causation, but rather should form the basis for additional research to establish if such correlations are indicative of causation. Only then can we get a sense as to how meaningful are the causes of the correlation. While the idea that data can speak for themselves free of bias or framing may seem like an attractive one, the reality is somewhat different. As Gould (1981: 166) notes, ‘inanimate data can never speak for themselves, and we always bring to bear some conceptual framework, either intuitive and illformed, or tightly and formally structured, to the task of investigation, analysis, and interpretation’.

In a provocative piece, Anderson argues that ‘the data deluge makes the scientific method obsolete’; that the patterns and relationships contained within big data inherently produce meaningful and insightful knowledge about social, political and economic processes and complex phenomena. He argues: There is now a better way. Petabytes allow us to say: ‘Correlation is enough.’ We can stop looking for models. We can analyze the data without hypotheses about what it might show. We can throw the numbers into the biggest computing clusters the world has ever seen and let statistical algorithms find patterns where science cannot... Correlation supersedes causation, and science can advance even without coherent models, unified theories, or really any mechanistic explanation at all. There’s no reason to cling to our old ways. (my emphasis) Similarly, Prensky (2009) argues: ‘scientists no longer have to make educated guesses, construct hypotheses and models, and test them with data-based experiments and examples. Instead, they can mine the complete set of data for patterns that reveal effects, producing scientific conclusions without further experimentation’ (my emphasis).

As argued in Chapter 1, data are not simply natural and essential elements that are abstracted from the world in neutral and objective ways and can be accepted at face value. Data do not pre-exist their generation and arise from nowhere. Rather data are created within a complex data assemblage that actively shapes its constitution. Data then can never just speak for themselves, but are always, inherently, speaking from a particular position (Crawford 2013). Further, Anderson’s (2008) claim that ‘[c]orrelation supersedes causation’, suggests that patterns found within a dataset are inherently meaningful. This is an assumption that all trained statisticians know is dangerous and false. Correlations between variables within a dataset can be random in nature and have no or little causal association (see Chapter 9). Interpreting every correlation as meaningful can therefore lead to serious ecological fallacies. This can be exacerbated in the case of big data because the empiricist position appears to promote the practice of data dredging – hunting for every correlation – thus increasing the likelihood of discovering random associations.

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The Financial Crisis and the Free Market Cure: Why Pure Capitalism Is the World Economy's Only Hope by John A. Allison

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A clear example of the proper use of mathematical models is physics. However, the models used in physics capture causal relationships and are properly evaluated based on the predictive power of these causal relationships. However, in economics, practically all mathematical models capture correlations, not causations. There is a difference in kind between correlation and causation. Also, the models are based on a multitude of assumptions. The danger lies in placing far too much confidence in models based on correlation rather than causation. Economists and government regulators often fall into the trap of believing that these models are objective. However, there are important economic factors, such as human behavior, that cannot be clearly mathematized. Taking these models as “gospel” is dangerous. There is also a tendency, in developing the models, to assume normal distributions with small “tails.”

In reality, the tails often turn out to be “fat,” that is, to have a greater chance of occurring than the model suggests. The tails typically represent very positive and very negative outcomes. In the case of the financial crisis, the negative fat tails (improbable events) became reality. These tails were magnified by the effect of panic on human behavior under stress. All the correlations (which were not based on causation) fell apart when human beings, who make decisions, started reacting to negative news. In addition, it is easy to underestimate the likelihood of unlikely events. For example, if you build a house in a 100-year flood plain, you will at some point experience a flood. It may be 90 years from now, or it may be next week. Eventually (or soon), a flood will affect your house. The mathematical models used by economists today are often floating abstractions that are not attached to reality.

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The Perfect Bet: How Science and Math Are Taking the Luck Out of Gambling by Adam Kucharski

By creating an explanation, we are assuming that one process has directly caused another. Horses in Hong Kong win because they are familiar with the terrain, and they are familiar with it because they have run lots of races. But just because two things are apparently related—like probability of winning and number of races run—it doesn’t mean that one directly causes the other. An oft-quoted mantra in the world of statistics is that “correlation does not imply causation.” Take the wine budget of Cambridge colleges. It turns out that the amount of money each Cambridge college spent on wine in the 2012–2013 academic year was positively correlated with students’ exam results during the same period. The more the colleges spent on wine, the better the results generally were. (King’s College, once home to Karl Pearson and Alan Turing, topped the wine list with a spend of £338,559, or about £850 per student.)

“Use of Performance Metrics to Forecast Success in the National Hockey League” (paper presented at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Prague, September 23–27, 2013). 205England had the lowest PDO: Burn-Murdoch, John. “Were England the Uunluckiest Team in the World Cup Group Stages?” FT Data Blog. 29 June 2014. http://blogs.ft.com/ftdata/2014/06/29/were-england-the-unluckiest-team-in-the-world-cup-group-stages/. 206Cambridge college spent on wine: “In Vino Veritas, Redux.” The Economist, February 5, 2014. http://www.economist.com/blogs/freeexchange/2014/02/correlation-and-causation-0. 207topped the wine list with a spend of £338,559: Simons, John. “Wages Not Wine: Booze Hound Colleges Spend £3 million on Wine.” Tab (Cambridge, England), January 22, 2014. http://thetab.com/uk/cambridge/2014/01/22/booze-hound-colleges-spend-3-million-on-wine-32441. 207Countries that consume lots of chocolate: Messerli, F. H. “Chocolate Consumption, Cognitive Function, and Nobel Laureates.”

See robots (bots) computerized prediction in blackjack, 42 in checkers, 156, 157 in horse racing, 46, 51, 57, 68 and the Monte Carlo method, 61 in roulette, 2, 13, 14, 15–20, 22 in sports, 80–82, 87, 88, 89–90, 97, 105, 217 “Computing Machinery and Intelligence” (Turing), 175 Connect Four, 158–159 control over events, 199 controlled randomness, 25–26, 28 cooperative relationships, 129, 136 copycats, 132 Coram, Marc, 63, 64 correlation and causation, issue of, 206–207 Corsi rating, 85 Cosmopolitan, Las Vegas 87 countermeasures, 21, 86, 195, 214 counting cards. See card counting Crick, Francis, 23 cricket, 90 curiosity, following, 218 Dahl, Fredrik, 172–173, 175, 176, 177, 182–183, 184, 185 Darwin, Charles, 46 data access to, 142 availability of, 54, 55, 68, 73, 86, 102, 174, 209 better, sports analysis methods and access to, 207, 217 binary, 116 collecting as much as possible, 4–5, 103 enough, to test strategies, 131 faster transatlantic travel of, 113 juggling, 166 limited, 84 new, testing strategies against, 53, 54 statistics and, importance of, in sports, 79, 80 storage and communication of, 11 data chunks, memory capacity and size of, 179–180 Deceptive Interaction Task, 190–191 decision making, chaotic, 162 decision-making layers, 173–174 Deep Blue chess computer, 166, 167, 171, 176 Deep Thought chess computer, 167 DeepFace Facebook algorithm, 174–175 DeRosa, David, 198–199, 200 Design of Experiments, The (Fisher), 24 deterministic game, 156 Diaconis, Persi, 62–63 DiCristina, Lawrence, 198, 199, 200, 201 Dixon, Mark, 74, 75, 76–78, 82, 97–98, 107, 218 Djokovic, Novak, 110 Dobson, Andrew, 129 Dodds, Peter Sheridan, 203 dogma, avoiding, 218 dovetail shuffle, 41–42, 62 Dow Jones Industrial Average, 96, 121, 122 Drug Enforcement Administration, 214 eBay, 94 Econometrica (journal), 148 economic theory, 153 ecosystems, 125–129, 130–131, 133 Einstein, Albert, 210 endgame database, 159–160 English Draughts Association, 156 English Premier League, 209 Enigma machines, 169–170 Eslami, Ali, 185–186, 187 Ethier, Stewart, 7–8 Eudaemonic Pie, The (Bass), 14, 15 Eudaemonic prediction method, 14, 15–20, 22, 124, 208 Euro 2008 soccer tournament, 76 European Championship (soccer tournament), 111 European currency union, 129 every-day gamblers, 102, 107 exchange rate, 110 exchanges.

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Keeping Up With the Quants: Your Guide to Understanding and Using Analytics by Thomas H. Davenport, Jinho Kim

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The degree of relatedness is expressed as a correlation coefficient, which ranges from −1.0 to +1.0. Correlation = +1 (Perfect positive correlation, meaning that both variables always move in the same direction together) Correlation = 0 (No relationship between the variables) Correlation = −1 (Perfect negative correlation, meaning that as one variable goes up, the other always trends downward) Correlation does not imply causation. Correlation is a necessary but insufficient condition for casual conclusions. Dependent variable: The variable whose value is unknown that you would like to predict or explain. For example, if you wish to predict the quality of a vintage wine using average growing season temperature, harvest rainfall, winter rainfall, and the age of the vintage, the quality of a vintage wine would be the dependent variable.

As we mentioned earlier in describing mad scientist experiments, if you create test and control groups and randomly assign people to them, if there turns out to be a difference in outcomes between the two groups, you can usually attribute it to being caused by the test condition. But if you simply find a statistical relationship between two factors, it’s unlikely to be a causal relationship. You may have heard the phrase, “correlation is not causation,” and it’s important to remember. Cognitive psychologists Christopher Chabris and Daniel Simons suggest a useful technique for checking on the causality issue in their book The Invisible Gorilla and Other Ways Our Intuitions Deceive Us: “When you hear or read about an association between two factors, think about whether people could have been assigned randomly to conditions for one of them.

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Big Data: A Revolution That Will Transform How We Live, Work, and Think by Viktor Mayer-Schonberger, Kenneth Cukier

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So the quarantine applies only to the individual Internet users whose searches were most highly correlated with having the flu. Here we have the data on whom to pick up. Federal agents, armed with lists of Internet Protocol addresses and mobile GPS information, herd the individual web searchers into quarantine centers. But as reasonable as this scenario might sound to some, it is just plain wrong. Correlations do not imply causation. These people may or may not have the flu. They’d have to be tested. They’d be prisoners of a prediction, but more important, they’d be victims of a view of data that lacks an appreciation for what the information actually means. The point of the actual Google Flu Trends study is that certain search terms are correlated with the outbreak—but the correlation may exist because of circumstances like healthy co-workers hearing sneezes in the office and going online to learn how to protect themselves, not because the searchers are ill themselves.

They were able to achieve their accomplishments because so many features of the city had been datafied (however inconsistently), allowing them to process the information. The inklings of experts had to take a backseat to the data-driven approach. At the same time, Flowers and his kids continually tested their system with veteran inspectors, drawing on their experience to make the system perform better. Yet the most important reason for the program’s success was that it dispensed with a reliance on causation in favor of correlation. “I am not interested in causation except as it speaks to action,” explains Flowers. “Causation is for other people, and frankly it is very dicey when you start talking about causation. I don’t think there is any cause whatsoever between the day that someone files a foreclosure proceeding against a property and whether or not that place has a historic risk for a structural fire. I think it would be obtuse to think so.

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Everything Is Obvious: *Once You Know the Answer by Duncan J. Watts

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With their own electronic sales databases, third-party ratings agencies like Nielsen and comScore, and the recent tidal wave of clickstream data online, advertisers can measure many more variables, and at far greater resolution, than Wanamaker could. Arguably, in fact, the advertising world has more data than it knows what to do with. No, the real problem is that what advertisers want to know is whether their advertising is causing increased sales; yet almost always what they measure is the correlation between the two. In theory, of course, everyone “knows” that correlation and causation are different, but it’s so easy to get the two mixed up in practice that we do it all the time. If we go on a diet and then subsequently lose weight, it’s all too tempting to conclude that the diet caused the weight loss. Yet often when people go on diets, they change other aspects of their lives as well—like exercising more or sleeping more or simply paying more attention to what they’re eating.

Both these strategies will have the effect that sales and advertising will tend to be correlated whether or not the advertising is causing anything at all. But as with the diet, it is the advertising effort on which the business focuses its attention; thus if sales or some other metric of interest subsequently increases, it’s tempting to conclude that it was the advertising, and not something else, that caused the increase.17 Differentiating correlation from causation can be extremely tricky in general. But one simple solution, at least in principle, is to run an experiment in which the “treatment”—whether the diet or the ad campaign—is applied in some cases and not in others. If the effect of interest (weight loss, increased sales, etc.) happens significantly more in the presence of the treatment than it does in the “control” group, we can conclude that it is in fact causing the effect.

Part of the problem is also that social scientists, like everyone else, participate in social life and so feel as if they can understand why people do what they do simply by thinking about it. It is not surprising, therefore, that many social scientific explanations suffer from the same weaknesses—ex post facto assertions of rationality, representative individuals, special people, and correlation substituting for causation—that pervade our commonsense explanations as well. MEASURING THE UNMEASURABLE One response to this problem, as Lazarsfeld’s colleague Samuel Stouffer noted more than sixty years ago, is for sociologists to depend less on their common sense, not more, and instead try to cultivate uncommon sense.10 But getting away from commonsense reasoning in sociology is easier said than done.

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The Health Gap: The Challenge of an Unequal World by Michael Marmot

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Should we really assume that these dark satanic mills and airless places, rather than causing terrible illness and shortened lives, selectively employed and attracted as residents sick people and those whose backgrounds accounted for all their subsequent illness? That subsequent improvement in living and working conditions, thus abating Victorian squalor, and associated improvements in health, were correlation, not causation? That while medical care improved health, housing also got better, and an intellectually slack public health profession mistook the improvement in housing and working conditions for causes of improved health? If proponents of this set of assumptions dropped their guard for a moment and accepted the evidence that air pollution, crowded living space, ghastly working conditions and poor nutrition were causes of ill-health in Victorian times why, a priori, do they start from the position that living and working conditions are not a cause of ill-health in the twenty-first century?

As well as having health insurance, 94 per cent had graduated from high school, and 43 per cent were college graduates. The ACE study was not a one-off. A review of 124 studies confirmed that child physical abuse, emotional abuse and neglect (they did not study sexual abuse) are linked to adult mental disorders, suicide attempts, drug use, sexually transmitted infections and risky sexual behaviour.9 The authors of the review concluded that this is more than simple correlation but represents causation. The graded nature of the relation between abuse and adult mental, and perhaps physical, ill-health – the more types of abuse the worse the adult health – suggests that we should not be looking only at exceptional episodes of abuse but, more generally, at quality of early child development. Indeed, further evidence supports this. Britain has been blessed by a series of long-term studies of people born at a particular moment and followed through their lives.

FIGURE 6.3: GETTING INTO WORK IN SWANSEA AND WREXHAM By focusing on the problem in a strategic way, working with young people, giving them access to information, and perhaps above all, caring, authorities in these towns lowered the toll of young people not in employment, education or training. There was an unexpected benefit. Youth offending in Swansea fell from over 1,000 incidents a year to fewer than 400.33 Correlation is not causation. One cannot say that the reduction in NEETs was responsible for the reduction in youth offending, but it is certainly possible. Unemployment harms health and work is vital. When work is of ‘good’ quality it is empowering. It provides power, money and resources – all essential for a healthy life. The ‘good’ characteristics of work tend to follow the social gradient: greater empowerment and better conditions go with higher status.

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Scale: The Universal Laws of Growth, Innovation, Sustainability, and the Pace of Life in Organisms, Cities, Economies, and Companies by Geoffrey West

Data for data’s sake, or the mindless gathering of big data, without any conceptual framework for organizing and understanding it, may actually be bad or even dangerous. Just relying on data alone, or even mathematical fits to data, without having some deeper understanding of the underlying mechanism is potentially deceiving and may well lead to erroneous conclusions and unintended consequences. This admonition is closely related to the classic warning that “correlation does not imply causation.” Just because two sets of data are closely correlated does not imply that one is the cause of the other. There are many bizarre examples that illustrate this point.4 For instance, over the eleven-year period from 1999 to 2010 the variation in the total spending on science, space, and technology in the United States almost exactly followed the variation in the number of suicides by hanging, strangulation, and suffocation.

With the advent of big data this classic view is being challenged. In a highly provocative article published in Wired magazine in 2008 titled “The End of Theory: The Data Deluge Makes the Scientific Method Obsolete,” its then editor, Chris Anderson, wrote: The new availability of huge amounts of data, along with the statistical tools to crunch these numbers, offers a whole new way of understanding the world. Correlation supersedes causation, and science can advance even without coherent models, unified theories, or really any mechanistic explanation at all . . . faced with massive data, this approach to science—hypothesize, model, test—is becoming obsolete. . . . Out with every theory of human behavior, from linguistics to sociology. Forget taxonomy, ontology, and psychology. Who knows why people do what they do? The point is they do it, and we can track and measure it with unprecedented fidelity.

There are many versions of these, but all of them are based on the idea that we can design and program computers and algorithms to evolve and adapt based on data input to solve problems, reveal insights, and make predictions. They all rely on iterative procedures for finding and building upon correlations in data without concern for why such relationships exist and implicitly presume that “correlation supersedes causation.” This approach has become a huge area of interest and has already had a big impact on our lives. For instance, it is central to how search engines like Google operate, how strategies for investment or operating an organization are devised, and it provides the foundational basis for driverless cars. It also brings up the classic philosophical question as to what extent these machines are “thinking.”

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Austerity: The History of a Dangerous Idea by Mark Blyth

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Rather, in both cases, what was once seen as sustainable suddenly became seen as unsustainable once the possibility of a contagion-led fire sale through the European bond markets was factored into a slow-moving growth crisis. As usual, it’s the perception of risk that matters. And again, just as we saw in the US case, there was no orgy of government spending behind all this. Why, then, keep up the fiction that the bond market crisis is a crisis of spendthrift governments? Confusing Correlation and Causation: Austerity’s Moment in the Sun With yields spiking to unsustainable levels in Greece, Ireland, and Portugal, each country received a bailout from the EU, ECB, and the IMF, as well as bilateral loans, on the condition that it accept and implement an austerity package to right its fiscal ship. Cut spending, raise taxes—but cut spending more than you raise taxes—and all will be well, the story went.

Growth rates and foreign investment both soared.105 Key to all this, as before, was the large expenditure-based cut plus wage moderation and devaluation.106 Stephen Kinsella offers a rather different version of events in his recent study of Ireland’s twin experiments with austerity: in the late 1980s and today in the aftermath of the banking crisis of 2008.107 Kinsella emphasizes that Ireland did have an expansion following a consolidation, as the literature claims, but notes that correlation is not causation in this case. Instead, he notes another correlation; that Ireland’s consolidation “coincided with a period of growth in the international economy, with the presence of fiscal transfers from the European Union, the opening up of the single market and a well-timed devaluation in August 1986.”108 An earlier paper by John Considine and James Duffy makes a similar point, namely, that it’s the boom in British imports—the so-called Lawson boom—that combined with the 1986 devaluation to make the difference.109 This is backed up by a piece by Roberto Perotti, who argues that in the Irish case “the concomitant depreciation of Sterling and the expansion in the UK … boosted Irish exports.”110 Kinsella also notes that the adjustment was considerably eased by an income tax amnesty that raised the equivalent of 2 percent of GDP.111 The part that stands out in Kinsella’s account is, however, something completely absent in other retellings of these events.

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Thinking, Fast and Slow by Daniel Kahneman

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The control group is expected to improve by regression alone, and the aim of the experiment is to determine whether the treated patients improve more than regression can explain. Incorrect causal interpretations of regression effects are not restricted to readers of the popular press. The statistician Howard Wainer has drawn up a long list of eminent researchers who have made the same mistake—confusing mere correlation with causation. Regression effects are a common source of trouble in research, and experienced scientists develop a healthy fear of the trap of unwarranted causal inference. One of my favorite examples of the errors of intuitive prediction is adapted from Max Bazerman’s excellent text Judgment in Managerial Decision Making: You are the sales forecaster for a department store chain. All stores are similar in size and merchandise selection, but their sales differ because of location, competition, and random factors.

income and education: The correlation appears impressive, but I was surprised to learn many years ago from the sociologist Christopher Jencks that if everyone had the same education, the inequality of income (measured by standard deviation) would be reduced only by about 9%. The relevant formula is v (1–r2), where r is the correlation. correlation and regression: This is true when both variables are measured in standard scores—that is, where each score is transformed by removing the mean and dividing the result by the standard deviation. confusing mere correlation with causation: Howard Wainer, “The Most Dangerous Equation,” American Scientist 95 (2007): 249–56. 18: Taming Intuitive Predictions far more moderate: The proof of the standard regression as the optimal solution to the prediction problem assumes that errors are weighted by the squared deviation from the correct value. This is the least-squares criterion, which is commonly accepted. Other loss functions lead to different solutions. 19: The Illusion of Understanding narrative fallacy: Nassim Nicholas Taleb, The Black Swan: The Impact of the Highly Improbable (New York: Random House, 2007).

Statistical Prediction: A Theoretical Analysis and a Review of the Evidence (Meehl) Clinton, Bill Coelho, Marta coffee mug experiments cognitive busyness cognitive ease; in basic assessments; and illusions of remembering; and illusions of truth; mood and; and writing persuasive messages; WYSIATI (what you see is all there is) and cognitive illusions; confusing experiences with memories; of pundits; of remembering; of skill; of stock-picking skill; of truth; of understanding; of validity Cognitive Reflection Test (CRT) cognitive strain Cohen, David coherence; see also associative coherence Cohn, Beruria coincidence coin-on-the-machine experiment cold-hand experiment Collins, Jim colonoscopies colostomy patients competence, judging of competition neglect complex vs. simple language concentration cogndiv height="0%"> “Conditions for Intuitive Expertise: A Failure to Disagree” (Kahneman and Klein) confidence; bias of, over doubt; overconfidence; WYSIATI (what you see is all there is) and confirmation bias conjunction fallacy conjunctive events, evaluation of “Consequences of Erudite Vernacular Utilized Irrespective of Necessity: Problems with Using Long Words Needlessly” (Oppenheimer) contiguity in time and place control cookie experiment correlation; causation and; illusory; regression and; shared factors and correlation coefficient cost-benefit correlation costs creativity; associative memory and credibility Csikszentmihalyi, Mihaly curriculum team Damasio, Antonio dating question Dawes, Robyn Day Reconstruction Method (DRM) death: causes of; life stories and; organ donation and; reminders of Deaton, Angus decisions, decision making; broad framing in; and choice from description; and choice from experience; emotions and vividness in; expectation principle in; in gambles, see gambles; global impressions and; hindsight bias and; narrow framing in; optimistic bias in; planning fallacy and; poverty and; premortem and; reference points in; regret and; risk and, see risk assessment decision utility decision weights; overweighting; unlikely events and; in utility theory vs. prospect theory; vivid outcomes and; vivid probabilities and decorrelated errors default options denominator neglect depression Detroit/Michigan problem Diener, Ed die roll problem dinnerware problem disclosures disease threats disgust disjunctive events, evaluation of disposition effect DNA evidence dolphins Dosi, Giovanni doubt; bias of confidence over; premortem and; suppression of Duke University Duluth, Minn., bridge in duration neglect duration weighting earthquakes eating eBay Econometrica economics; behavioral; Chicago school of; neuroeconomics; preference reversals and; rational-agent model in economic transactions, fairness in Econs and Humans Edge Edgeworth, Francis education effectiveness of search sets effort; least, law of; in self-control ego depletion electricity electric shocks emotional coherence, see halo effect emotional learning emotions and mood: activities and; affect heuristic; availability biases and; in basic assessments; cognitive ease and; in decision making; in framing; mood heuristic for happiness; negative, measuring; and outcomes produced by action vs. inaction; paraplegics and; perception of; substitution of question on; in vivid outcomes; in vivid probabilities; weather and; work and employers, fairness rules and endangered species endowment effect; and thinking like a trader energy, mental engagement Enquiry Concerning Human Understanding, An (Hume) entrepreneurs; competition neglect by Epley, Nick Epstein, Seymour equal-weighting schemes Erev, Ido evaluability hypothesis evaluations: joint; joint vs. single; single evidence: one-sided; of witnesses executive control expectation principle expectations expected utility theory, see utility theory experienced utility experience sampling experiencing self; well-being of; see also well-being expert intuition; evaluating; illusions of validity of; overconfidence and; as recognition; risk assessment and; vs. statistical predictions; trust in expertise, see skill Expert Political Judgment: How Good Is It?

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The Stuff of Thought: Language as a Window Into Human Nature by Steven Pinker

But as we will see in chapter 5, people have the cognitive means to evaluate whether a framing is faithful to reality; the framing does not lock their minds into one way of construing the world. 3. The stock of words in a language reflects the kinds of things its speakers deal with in their lives and hence think about. This, of course, is the obvious non-Whorfian interpretation of the Eskimo-snow factoid. The Whorfian interpretation is a classic example of the fallacy of confusing correlation with causation. In the case of varieties of snow and words for snow, not only did the snow come first, but when people change their attention to snow, they change their words as a result. That’s how meteorologists, skiers, and New Englanders coin new expressions for the stuff, whether in circumlocutions (wet snow, sticky snow) or in neologisms (hardpack, powder, dusting, flurries). Presumably it didn’t happen the other way around—that vocabulary show-offs coined new words for snow, then took up skiing or weather forecasting because they were intrigued by their own coinages.

(All of these are signatures of the analogue estimation system—which reinforces the notion that this component of the number sense exists independently of number words.) Gordon concluded that the lack of precise number thoughts among the Pirahã is caused by their lack of precise number words—the “rare and perhaps unique case for strong linguistic determinism.” But as the cognitive scientist Daniel Casasanto put it, this is a case of “crying Whorf ”: it depends on a dubious leap from correlation to causation.94 It can’t be a coincidence that the Pirahã language just happens to lack big number words (unlike the English language) and the Pirahã speakers just happen to hunt and gather in remote stone-age villages (unlike English speakers). A more plausible interpretation is that the lifestyle, history, and culture of a technologically undeveloped hunter-gatherer people will cause it to lack both number words and numerical reasoning.

It follows, then, that all reasonings concerning cause and effect are founded on experience, and that all reasonings from experience are founded on the supposition that the course of nature will continue uniformly the same.109 Tucked into this analysis of whether we can justify our causal attributions is an offhand theory of the psychology of causality called constant conjunction: that our intuitions of cause and effect are nothing but an expectation that if one thing followed another many times in the past, it will continue to do so in the future. It’s not terribly different from what happens when a dog is conditioned to anticipate food when a bell is rung, or a pigeon learns to peck a key in the expectation of food. The story that began the chapter, about the two alarms that go off in succession, raises an obvious problem for the theory. People understand (even if they don’t always apply) the principle that correlation does not imply causation. The rooster’s cock-a-doodle-doo does not cause the sun to rise, thunder doesn’t cause forest fires, and the flashing lights on the top of a printer don’t cause it to spit out a document. These are perceived to be epiphenomena: byproducts of the real causes. I called Hume’s theory “offhand” because he didn’t consistently embrace it himself. The very example of “causation” he adduced in his summary—“when we think of the son, we are apt to carry our attention to the father”—could not be a more ruinous counterexample.

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Singularity Rising: Surviving and Thriving in a Smarter, Richer, and More Dangerous World by James D. Miller

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If Gates is correct, then the best way to help the world’s destitute might be to figure out how to raise the IQs of the world’s poorest people. GENES VS. ENVIRONMENT Genetics determines between 50 and 80 percent of your IQ. To be more precise: intelligence researchers disagree over how much of the variation in people’s IQs is caused by genetics, with estimates ranging from about 50 to 80 percent.162 Researchers don’t agree on the relative importance of genetics in determining IQ because of the challenge of separating correlation from causation. To understand this difficulty, suppose we know that parents who read a lot to their children tend to have children with high IQs. This correlation might occur because reading to a child increases her IQ. But here are some other possible causes, and if any one of them is the correct explanation, reading will do absolutely nothing to boost a child’s intelligence: •The higher a parent’s IQ, the more she enjoys reading to her child, and so the more she will read to her child.

Researchers have some decent evidence that brain training can reduce the risk of an elderly person developing dementia.278 Given the huge economic burden that dementia imposes on the United States, if brain training proved effective, it could reduce the rate of increase of Medicare costs. A child’s working memory has been found to be a key predictor of his success in kindergarten as measured by teacher evaluations, perhaps indicating that parents should provide brain training to their toddlers.279 Of course, the relationship between these two indicators might be due merely to correlation, not causation, and so using brain fitness software to improve a four-year-old’s working memory might not help him in kindergarten. If computer brain training proved effective, educators could continually improve it using massive data analysis. Brain-training programs could easily keep track of students’ performances. Researchers could use this data to figure out what types of exercises worked best for different categories of students.

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The Future of the Brain: Essays by the World's Leading Neuroscientists by Gary Marcus, Jeremy Freeman

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Furthermore, experiments with appropriately modified viruses to stain, mark, turn on, or turn off molecularly identified subpopulations of neurons permit unprecedented control of mouse brain circuitry. This cannot be emphasized enough. The exploding use of opto- and pharmacogenetics methods that delicately, transiently, reversibly, and invasively control defined events in defined cell types at defined times constitute a suite of interventionist tools that allows neuroscience to move from correlation to causation, from observing that this circuit is activated whenever the subject is contemplating a decision to inferring that this circuit is necessary for decision making. Second, the human brain is more than three orders of magnitude larger than the mouse brain—1.4 kg weight versus 0.4 g; a 1-liter volume versus a sugar cube; eighty-six billion nerve cells versus seventy-one million for the entire brain and sixteen billion versus fourteen million nerve cells for the neocortex.

The blurriness of these instruments was mirrored by the primitive and edentate tools used to safely perturb the human brain—electrical stimulation in patients, and extracranial electromagnetic fields and drugs in volunteers. The other major advance fifty years ago was the birth of opto- and pharmaco-genetics, methods that delicately, transiently, reversibly, and invasively control defined events in defined cell types at defined times, initially in a few model organisms—the worm, the fly, and the mouse. Equipped with these tools for perturbing the brain, scientists systematically moved from correlation to causation, from observing that this circuit is activated whenever the subject is contemplating a decision to inferring that this circuit is necessary for decision making or that those neurons mark a particular memory. By the early 2020s, the complete logic of thalamo-cortical circuits could be manipulated, in hindsight a tipping point in our ability to bridge the gap between cortex and theories of its universal and particular functions.

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This Will Make You Smarter: 150 New Scientific Concepts to Improve Your Thinking by John Brockman

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If, for most reasonable sets of priors, information about A would allow us to update our estimate of B, then it would seem there is some sort of causal connection between the two. But the form of the causal connection is unspecified—a principle often stated as “correlation does not imply causation.” The reason for this is that the essence of causation as a concept rests on our tendency to have information about earlier events before we have information about later events. (The full implications of this concept for human consciousness, the second law of thermodynamics, and the nature of time are interesting, but sadly outside the scope of this essay.) If information about all events always came in the order in which the events occurred, then correlation would indeed imply causation. But in the real world, not only are we limited to observing events in the past but also we may discover information about those events out of order.

., 61 climate change, 51, 53, 99, 178, 201–2, 204, 268, 309, 315, 335, 386, 390 CO2 levels and, 202, 207, 217, 262 cultural differences in view of, 387–88 global economy and, 238–39 procrastination in dealing with, 209, 210 clinical trials, 26, 44, 56 cloning, 56, 165 coastlines, xxvi, 246 Cochran, Gregory, 360–62 coffee, 140, 152, 351 cognition, 172 perception and, 133–34 cognitive humility, 39–40 cognitive load, 116–17 cognitive toolkit, 333 Cohen, Daniel, 254 Cohen, Joel, 65 Cohen, Steven, 307–8 cold fusion, 243, 244 Coleman, Ornette, 254, 255 collective intelligence, 257–58 Colombia, 345 color, 150–51 color-blindness, 144 Coltrane, John, 254–55 communication, 250, 358, 372 depth in, 227 temperament and, 231 companionship, 328–29 comparative advantage, law of, 100 comparison, 201 competition, 98 complexity, 184–85, 226–27, 326, 327 emergent, 275 computation, 227, 372 computers, 74, 103–4, 146–47, 172 cloud and, 74 graphical desktops on, 135 memory in, 39–40 open standards and, 86–87 computer software, 80, 246 concept formation, 276 conduction, 297 confabulation, 349–52 confirmation bias, 40, 134 Conner, Alana, 367–70 Conrad, Klaus, 394 conscientiousness, 232 consciousness, 217 conservatism, 347, 351 consistency, 128 conspicuous consumption, 228, 308 constraint satisfaction, 167–69 consumers, keystone, 174–76 context, sensitivity to, 40 continental drift, 244–45 conversation, 268 Conway, John Horton, 275, 277 cooperation, 98–99 Copernicanism, 3 Copernican Principle, 11–12, 25 Copernicus, Nicolaus, 11, 294 correlation, and causation, 215–17, 219 creationism, 268–69 creativity, 152, 395 constraint satisfaction and, 167–69 failure and, 79, 225 negative capability and, 225 serendipity and, 101–2 Crick, Francis, 165, 244 criminal justice, 26, 274 Croak, James, 271–72 crude look at the whole (CLAW), 388 Crutzen, Paul, 208 CT scans, 259–60 cultural anthropologists, 361 cultural attractors, 180–83 culture, 154, 156, 395 change and, 373 globalization and, see globalization culture cycle, 367–70 cumulative error, 177–79 curating, 118–19 currency, central, 41 Cushman, Fiery, 349–52 cycles, 170–73 Dalrymple, David, 218–20 DALYs (disability-adjusted life years), 206 danger, proving, 281 Darwin, Charles, 2, 44, 89, 98, 109, 156, 165, 258, 294, 359 Das, Satyajit, 307–9 data, 303, 394 personal, 303–4, 305–6 security of, 76 signal detection theory and, 389–93 Dawkins, Richard, 17–18, 180, 183 daydreaming, 235–36 DDT, 125 De Bono, Edward, 240 dece(i)bo effect, 381–85 deception, 321–23 decision making, 52, 305, 393 constraint satisfaction and, 167–69 controlled experiments and, 25–27 risk and, 56–57, 68–71 skeptical empiricism and, 85 deduction, 113 defeasibility, 336–37 De Grey, Aubrey, 55–57 delaying gratification, 46 democracy, 157–58, 237 Democritus, 9 Demon-Haunted World, The (Sagan), 273 Dennett, Daniel C., 170–73, 212, 275 depth, 226–28 Derman, Emanuel, 115 Descent of Man, The (Darwin), 156 design: mind and, 250–53 recursive structures in, 246–49 determinism, 103 Devlin, Keith, 264–65 Diagnostic and Statistical Manual of Mental Disorders (DSM-5), 233–34 “Dial F for Frankenstein” (Clarke), 61 Diesel, Rudolf, 170 diseases, 93, 128, 174 causes of, 59, 303–4 distributed systems, 74–77 DNA, 89, 165, 223, 244, 260, 292, 303, 306 Huntington’s disease and, 59 sequencing of, 15 see also genes dopamine, 230 doughnuts, 68–69, 70 drug trade, 345 dualities, 296–98, 299–300 wave-particle, 28, 296–98 dual view of ourselves, 32 dynamics, 276 Eagleman, David, 143–45 Earth, 294, 360 climate change on, see climate change distance between sun and, 53–54 life on, 3–5, 10, 15 earthquakes, 387 ecology, 294–95 economics, 100, 186, 208, 339 economy(ies), 157, 158, 159 global, 163–64, 238–39 Pareto distributions in, 198, 199, 200 and thinking outside of time, 223 ecosystems, 312–14 Edge, xxv, xxvi, xxix–xxx education, 50, 274 applying to real-world situations, 40 as income determinant, 49 policies on, controlled experiments in, 26 scientific lifestyle and, 20–21 efficiency, 182 ego: ARISE and, 235–36 see also self 80/20 rule, 198, 199 Einstein, Albert, 28, 55, 169, 301, 335, 342 on entanglement, 330 general relativity theory of, 25, 64, 72, 234, 297 memory law of, 252 on simplicity, 326–27 Einstellung effect, 343–44 electrons, 296–97 Elliott, Andrew, 150 Eliot, T.

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What the F: What Swearing Reveals About Our Language, Our Brains, and Ourselves by Benjamin K. Bergen

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For example, here’s a graph of the age at which one particular child first used each of his nouns (his age is on the x-axis) plotted against how frequent that word was in the child-directed speech he heard (it’s actually the log of word frequency because frequency effects in language have logarithmic effects).21 You can see that within nouns, the child learns more frequent ones earlier, on average, and then moves on to learn less frequent ones as well. Each dot represents the first time the child produced a particular noun; more frequent nouns tended to be learned earlier than less frequent ones. Image reproduced from B. C. Roy et al. (2009), used with permission. Of course, a reasonable person could object to studies like this one. Correlation does not imply causation. So the fact that children tend to learn more frequent words earlier doesn’t entail that frequency is the reason for earlier word learning. Other factors might be in play. For instance, more frequent words are shorter, all things being equal. And children learn shorter words earlier. Maybe frequency plays no causal role. To know for sure, you’d need to run an experiment: you’d have to manipulate how often children heard particular words and see whether this factor alone, holding all other possible causes constant, affected children’s learning of the words.

The study states that adolescents who reported watching shows and playing games with more profanity in them also reported finding profanity more acceptable and using more profanity themselves. Does this answer the question about frequency? Does this mean that exposure to more profanity leads to more use of profanity? We don’t know, because the study was correlational. It’s not always obvious why correlation doesn’t imply causation, so let me just remind you here. (If this is old hat to you, by all means, skip to the next paragraph.) Here’s a nice example of why you can’t infer causation from correlation.24 Suppose you want to know whether religious faith causes an increase in alcohol consumption. You might try to find an answer by counting the number of bars and the number of churches in each of a large number of US cities.

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Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy by Cathy O'Neil

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Insurance companies as well as bankers delineated neighborhoods where they would not invest. This cruel practice, known as redlining, has been outlawed by various pieces of legislation, including the Fair Housing Act of 1968. Nearly a half century later, however, redlining is still with us, though in far more subtle forms. It’s coded into the latest generation of WMDs. Like Hoffman, the creators of these new models confuse correlation with causation. They punish the poor, and especially racial and ethnic minorities. And they back up their analysis with reams of statistics, which give them the studied air of evenhanded science. On this algorithmic voyage through life, we’ve clawed our way through education and we’ve landed a job (even if it is one that runs us on a chaotic schedule). We’ve taken out loans and seen how our creditworthiness is a stand-in for other virtues or vices.

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The Euro: How a Common Currency Threatens the Future of Europe by Joseph E. Stiglitz, Alex Hyde-White

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Supporters of the euro might respond by pointing out that if Greece owed money to, say, Germany in Germany’s currency, the weakening of Greece’s exchange rate would increase the real indebtedness of Greece. True—but that is precisely what is happening now, as Troika policies have lowered Greek incomes by more than a quarter. More relevant, Greece would likely not have borrowed in German currency, precisely because it (and presumably its lenders) should have been aware of the risk that that entailed.30 CORRELATION AND CAUSATION The poor performance of the eurozone, both absolutely and relative to others, might, of course, be due to some factor other than the euro. And there have been changes in the global economy that have affected the eurozone and, more particularly, one group of countries within the eurozone relative to others. That’s why Germany’s suggestion that the failures of the countries in the eurozone are due to their profligacy seems so out of touch with economic reality, so demonstrative of a total lack of analysis.

They argued that there were important instances where when governments had contracted government spending, the result was that the overall economy grew. The notion that there could be expansionary contractions was a chimera. A series of papers showed major flaws in their analysis.57 The IMF, which had supported austerity-style policies in the past, in fact reversed itself. It pointed out that when governments contract spending, the economy contracts.58 The big flaw in the pro-austerity study was confusing correlation with causation. There were a few countries, small economies with flexible exchange rates, where a contraction in government spending was associated with growth; but in these cases the hole in demand created by the government contraction was filled in with exports. Canada in the early 1990s was lucky because the United States was going through a rapid expansion, the recovery from the 1991 recession. Canada benefited, too from a flexible exchange rate that enabled it to sell its goods more cheaply to the United States.

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The Better Angels of Our Nature: Why Violence Has Declined by Steven Pinker

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When rock music burst onto the scene in the 1950s, politicians and clergymen vilified it for corrupting morals and encouraging lawlessness. (An amusing video reel of fulminating fogies can be seen in Cleveland’s Rock and Roll Hall of Fame and Museum.) Do we now have to—gulp—admit they were right? Can we connect the values of 1960s popular culture to the actual rise in violent crimes that accompanied them? Not directly, of course. Correlation is not causation, and a third factor, the pushback against the values of the Civilizing Process, presumably caused both the changes in popular culture and the increase in violent behavior. Also, the overwhelming majority of baby boomers committed no violence whatsoever. Still, attitudes and popular culture surely reinforce each other, and at the margins, where susceptible individuals and subcultures can be buffeted one way or another, there are plausible causal arrows from the decivilizing mindset to the facilitation of actual violence.

Archer found that countries in which women are better represented in government and the professions, and in which they earn a larger proportion of earned income, are less likely to have women at the receiving end of spousal abuse. Also, cultures that are classified as more individualistic, where people feel they are individuals with the right to pursue their own goals, have relatively less domestic violence against women than the cultures classified as collectivist, where people feel they are part of a community whose interests take precedence over their own.94 These correlations don’t prove causation, but they are consistent with the suggestion that the decline of violence against women in the West has been pushed along by a humanist mindset that elevates the rights of individual people over the traditions of the community, and that increasingly embraces the vantage point of women. Though elsewhere I have been chary about making predictions, I think it’s extremely likely that in the coming decades violence against women will decrease throughout the world.

On the contrary, “they must be permitted . . . the foolish and childish actions suitable to their years.”168 The idea that the way children are treated determines the kinds of adults they grow into is conventional wisdom today, but it was news at the time. Several of Locke’s contemporaries and successors turned to metaphor to remind people about the formative years of life. John Milton wrote, “The childhood shows the man as morning shows the day.” Alexander Pope elevated the correlation to causation: “Just as the twig is bent, the tree’s inclined.” And William Wordsworth inverted the metaphor of childhood itself: “The child is father of the man.” The new understanding required people to rethink the moral and practical implications of the treatment of children. Beating a child was no longer an exorcism of malign forces possessing a child, or even a technique of behavior modification designed to reduce the frequency of bratty behavior in the present.

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The Collapse of Western Civilization: A View From the Future by Naomi Oreskes, Erik M. Conway

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fisherian statistics A form of mathematical analysis developed in the early twentieth century and designed to help distinguish between causal and accidental relation-ships between phenomena. Its originator, R. A. Fisher, was one of the founders of the science of population genetics, and also an advocate of racially-based eugenics programs. Fisher also rejected the evidence that tobacco use caused cancer, and his argument that “correlation is not causation” was later used as a mantra by neoliberals rejecting the scientific evidence of various forms of adverse environmental and health effects from industrial products (see statistical significance). fugitive emissions Leakage from wellheads, pipelines, refineries, etc. Considered “fugitive” because the releases were supposedly unintentional, at least some of them (e.g., methane venting at oil wells) were in fact entirely deliberate.

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Wall Street: How It Works And for Whom by Doug Henwood

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A policy-led decline in interest rates pushes up stock prices, and stockholding households spend more. Recently, however, that relationship seems to have broken down. Why this should be isn't clear; it could be that both the stock market and consumer spending were independently responding to lower interest rates, and that the conclusion that stocks were "causing" the spending changes are a classic example of confusing correlation with causation. Or it may be that the increasing institutionalization of the market has reduced the effect of stock prices on personal spending. Or it may have been that household balance sheets were in such terrible shape in the early 1990s that a bull market was of little help (Steindel 1992). But whatever the reason, this household application of q theory isn't quite as impressive as it once was.

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Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die by Eric Siegel

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Public health offices in the UK Band members benefit from peer support and solo artists exhibit even riskier behaviour. Correlation Does Not Imply Causation Satisfaction came in the chain reaction. —From the song “Disco Inferno,” by The Trammps The preceding tables, packed with fun-filled facts, do not explain a single thing. Take note, the third column is headed “Suggested Explanation.” The left column’s discoveries are real, validated by data, but the reasons behind them are unknown. Every explanation put forth, each entry in the rightmost column, is pure conjecture with absolutely no hard facts to back it up. The dilemma is, as it is often said, correlation does not imply causation.5 The discovery of a predictive relationship between A and B does not mean one causes the other, not even indirectly.

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Even among the “bottom billion”—the population of countries that have experienced the weakest economic growth over the last few decades—quality of life has increased dramatically. In 1950, life expectancy in sub-Saharan Africa was just 36.7 years. Now it’s 56 years, a gain of almost 50 percent. The picture that Dambisa Moyo paints is inaccurate. In reality, a tiny amount of aid has been spent, and there have been dramatic increases in the welfare of the world’s poorest people. Of course, correlation is not causation. Merely showing that the people’s welfare has improved at the same time the West has been offering aid does not prove that aid caused the improvement. It could be that aid is entirely incidental, or even harmful, holding back even greater progress that would have happened anyway or otherwise. But in fact there’s good reason to think that, on average, international aid spending has been incredibly beneficial.

Robustness of evidence is very important for the simple reason that many programs don’t work, and it’s hard to distinguish the programs that don’t work from the programs that do. If we’d assessed Scared Straight by looking just at before-and-after delinquency rates for individuals who went through the program, we would have concluded it was a great program. Only after looking at randomized controlled trials could we tell that correlation did not indicate causation in this case and that Scared Straight programs were actually doing more harm than good. One of the most damning examples of low-quality evidence concerns microcredit (that is, lending small amounts of money to the very poor, a form of microfinance most famously associated with Muhammad Yunus and the Grameen Bank). Intuitively, microcredit seems like it would be very cost-effective, and there were many anecdotes of people who’d received microloans and used them to start businesses that, in turn, helped them escape poverty.

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Half the Sky: Turning Oppression Into Opportunity for Women Worldwide by Nicholas D. Kristof, Sheryl Wudunn

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The methodology of such studies is typically weak, and it doesn’t adequately account for cause and effect. “The evidence, in most cases, suffers from obvious biases: educated girls come from richer families and marry richer, more educated, more progressive husbands,” notes Esther Duflo of MIT, one of the most careful scholars of gender and development. “As such, it is, in general, difficult to account for all these factors, and few of the studies have tried to do so.” Correlation, in short, is not causation.* Advocates also undermine the trustworthiness of their cause by cherry-picking evidence. While we argue that schooling girls does stimulate economic growth and foster stability, for example, it is also true that one of the most educated parts of rural India is the state of Kerala, which has stagnated economically. Likewise, two of the places in the Arab world that have given girls the most education were Lebanon and Saudi Arabia, yet the former has been a vortex of conflict and the latter a breeding ground for violent fundamentalists.

This was something that we didn’t expect at all. It shows the power of education.” Speaking of role models and the power of education, Camfed Zimbabwe has a new and dynamic executive director. She’s a young woman who knows something about overcoming long odds and the impact a few dollars in tuition assistance can make in a girl’s life. It’s Angeline. * Larry Summers offers an example to emphasize the distinction between correlation and causation. He notes that there is an almost perfect correlation between literacy and ownership of dictionaries. But handing out more dictionaries will not raise literacy. CHAPTER ELEVEN Microcredit: The Financial Revolution It is impossible to realize our goals while discriminating against half the human race. As study after study has taught us, there is no tool for development more effective than the empowerment of women.

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Dreamland: The True Tale of America's Opiate Epidemic by Sam Quinones

This was preposterous. Never in thirty years of statistical mechanics had Orman Hall heard of a correlation that close to 1.0, which was almost as if the charts were saying that dispensing prescription painkillers was the same thing as people dying. Gay couldn’t believe it either. He ran the DOH numbers again. Each time, 0.979 appeared on his computer screen. Every statistician knows correlation does not mean causation. But to Gay the correlations did mean that Ohio could all but predict one overdose death for roughly every two months’ worth of prescription opiates dispensed. A Pro Wrestler’s Legacy Seattle, Washington In 2007, Alex Cahana opened the door to what had been John Bonica’s Center for Pain Relief at the University of Washington and found a cobwebbed relic. The pathbreaking clinic was now in a windowless basement.

In fact, fewer medical students were going into primary care—repelled by long hours, the modest money, and the lack of respect. One study estimated the country would need fifty-two thousand more primary care docs by 2025. A commentary by four doctors and researchers in the American Journal of Public Health in September 2014 insisted that “It is difficult to believe that the parallel rise in prescriptions and associated harms is mere correlation without causation. [Also] it is difficult to believe that the problem is solely attributable to patients with already existing substance use disorders.” They went on, “Appropriate medical use of prescription opioidscan, in some unknown proportion of cases, initiate a progression toward misuse and ultimately addiction . . . Even if an initial exposure is insufficient to cause addiction directly, perhaps it is sufficient to trigger initial misuse that could ultimately lead to addiction.”

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Hubris: Why Economists Failed to Predict the Crisis and How to Avoid the Next One by Meghnad Desai

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The 4-Hour Body: An Uncommon Guide to Rapid Fat-Loss, Incredible Sex, and Becoming Superhuman by Timothy Ferriss

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The point isn’t to speculate about hundreds of possible explanations. The point is to be skeptical, especially of sensationalist headlines. Most “new studies” in the media are observational studies that can, at best, establish correlation (A happens while B happens), but not causality (A causes B to happen). If I pick my nose when the Super Bowl cuts to a commercial, did I cause that? This isn’t a haiku. It’s a summary: correlation doesn’t prove causation. Be skeptical when people tell you that A causes B. They’re wrong much more than 50% of the time. USE THE YO-YO: EMBRACE CYCLING Yo-yo dieting gets a bad rap. Instead of beating yourself up, going to the shrink, or eating an entire cheesecake because you ruined your diet with one cookie, allow me to deliver a message: it’s normal. Eating more, then less, then more, and so on in a continuous sine wave is an impulse we can leverage to reach goals faster.

Here is the most important paragraph in this chapter: Observational studies cannot control or even document all of the variables involved. Observational studies can only show correlation: A and B both exist at the same time in one group. They cannot show cause and effect.4 In contrast, randomized and controlled experiments control variables and can therefore show cause and effect (causation): A causes B to happen. The satirical religion Pastafarianism purposely confuses correlation and causation: With a decrease in the number of pirates, there has been an increase in global warming over the same period. Therefore, global warming is caused by a lack of pirates. Even more compelling: Somalia has the highest number of Pirates AND the lowest Carbon emissions of any country. Coincidence? Drawing unwarranted cause-and-effect conclusions from observational studies is the bread-and-butter of media and cause- or financially-driven scientists blind to their own lack of ethics.

Then try to bundle all the data up together, so that your negative data is swallowed up by some mediocre positive results. Or you could get really serious and start to manipulate the statistics. For two pages only, this will now get quite nerdy. Here are the classic tricks to play in your statistical analysis to make sure your trial has a positive result. Ignore the protocol entirely Always assume that any correlation proves causation. Throw all your data into a spreadsheet programme and report—as significant—any relationship between anything and everything if it helps your case. If you measure enough, some things are bound to be positive just by sheer luck. Play with the baseline Sometimes, when you start a trial, quite by chance the treatment group is already doing better than the placebo group. If so, then leave it like that.

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The New Kingmakers by Stephen O'Grady

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Seventeen months into its existence, Android was an interesting project, but an also-ran next to Apple’s iPhone OS (it was not renamed iOS until June 2010). Google understood that developers are more likely to build for themselves—what’s referred to in the industry as “scratching their own itch”—Google made sure that several thousand developers motivated enough to attend their conference had an Android device to use for themselves. The statistics axiom that correlation does not prove causation certainly applies here, but it’s impossible not to notice the timing of that handset giveaway. On the day that Google sent all of those I/O attendees home happy, the number of Android devices being activated per day was likely in the low tens of thousands (Google hasn’t made this data available). By the time the conference rolled around again a year later, the number was around 100,000.

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Both are in the data collection and targeting business, and Silicon Valley collects heaps of data which the NSA would love to have.* Silicon Valley is merely targeting consumers with ads and prompts and nudges that might get them to click or to buy something. They are bound together by common interests, philosophies, and methods. One of the main problems with Big Data is that it produces correlations but not causations. We learn that two things seem to be related—for example, that people with a specific set of personal characteristics are prone to depression or bad driving—but we don’t learn why. This is ironic given that Big Data is the ultimate fact-producing discipline: it promises answers, actionable ones. But data itself can be messy and often must be smoothed over, interpreted, supplemented.

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The Numerati by Stephen Baker

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A man can write like a woman, he says, but does he buy like a woman? Sifry goes on at length about the dangers of predicting people's behavior based on statistical correlations. "Let's say that according to my analytics, you said that Mission Impossible III was no good and that you can't wait to see Prairie Home Companion," he says. "I can't assume from that that you're an NPR listener. That's where you get into trouble." That's mistaking correlation for causation, he says. It's common among data miners—and most other humans. How many times have you heard people say, "They always do that..."? For Kaushansky, putting his skateboarding friend and a few others in the wrong tribes may not turn out to be too serious. That's why advertising and marketing are such wonderful testing grounds for the Numerati. If they screw up, the only harm is that we see the wrong ad or receive irrelevant coupons.

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A Mathematician Plays the Stock Market by John Allen Paulos

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To find the volatility of a portfolio in general, we need what is called the “covariance” (closely related to the correlation coefficient) between any pair of stocks X and Y in the portfolio. The covariance between two stocks is roughly the degree to which they vary together—the degree, that is, to which a change in one is proportional to a change in the other. Note that unlike many other contexts in which the distinction between covariance (or, more familiarly, correlation) and causation is underlined, the market generally doesn’t care much about it. If an increase in the price of ice cream stocks is correlated to an increase in the price of lawn mower stocks, few ask whether the association is causal or not. The aim is to use the association, not understand it—to be right about the market, not necessarily to be right for the right reasons. Given the above distinction, some of you may wish to skip the next three paragraphs on the calculation of covariance.

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The Biology of Belief: Unleashing the Power of Consciousness, Matter & Miracles by Bruce H. Lipton

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What about all those headlines trumpeting the discovery of a gene for everything from depression to schizophrenia? Read those articles closely and you’ll see that behind the breathless headline is a more sober truth. Scientists have linked lots of genes to lots of different diseases and traits, but scientists have rarely found that one gene causes a trait or a disease. The confusion occurs when the media repeatedly distort the meaning of two words: correlation and causation. It’s one thing to be linked to a disease; it’s quite another to cause a disease, which implies a directing, controlling action. If I show you my keys and say that a particular key “controls” my car, you at first might think that makes sense because you know you need that key to turn on the ignition. But does the key actually “control” the car? If it did, you couldn’t leave the key in the car alone because it might just borrow your car for a joy ride when you are not paying attention.

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Mastering Pandas by Femi Anthony

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Correlation is the general term we use in statistics for variables that express dependence with each other. We can then use this relationship to try and predict the value of one set of variables from the other; this is termed as regression. Correlation The statistical dependence expressed in a correlation relationship does not imply a causal relationship between the two variables; the famous line on this is "Correlation does not imply Causation". Thus, correlation between two variables or datasets implies just a casual rather than a causal relationship or dependence. For example, there is a correlation between the amount of ice cream purchased on a given day and the weather. For more information on correlation and dependency, refer to http://en.wikipedia.org/wiki/Correlation_and_dependence. The correlation measure, known as correlation coefficient, is a number that captures the size and direction of the relationship between the two variables.

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The Achievement Habit: Stop Wishing, Start Doing, and Take Command of Your Life by Bernard Roth

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A woman comes up to him after some time and says, “Pardon me, sir, why are you snapping your fingers?” He replies, “I am keeping the tigers away.” She says, “Sir, except for the zoo, there’s not a tiger for thousands of miles.” “Pretty effective, isn’t it?” he says. This joke uses what is called a causal fallacy. The fallacy comes because the finger snapper mistakenly believes that correlation implies causation. This is just one of several logical fallacies in which two events that occur at the same time are taken to have a cause-and-effect relationship. This version of the fallacy is also known as cum hoc ergo propter hoc (Latin for “with this, therefore because of this”) or, simply, false cause. A similar fallacy—that an event that follows another was a consequence of the first—is described as post hoc ergo propter hoc (Latin for “after this, therefore because of this”).

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Small Data: The Tiny Clues That Uncover Huge Trends by Martin Lindstrom

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A source who works at Google once confessed to me that despite the almost 3 billion humans who are online,4 and the 70 percent of online shoppers who go onto Facebook daily,5 and the 300 hours of videos on YouTube (which is owned by Google) uploaded every minute,6 and the fact that 90 percent of all the world’s data has been generated over the last two years.7 Google ultimately has only limited information about consumers. Yes, search engines can detect unusual correlations (as opposed to causations). With 70 percent accuracy, my source tells me, software can assess how people feel based on the way they type, and the number of typos they make. With 79 percent precision, software can determine a user’s credit rating based on the degree to which they write in ALL CAPS. Yet even with all these stats, Google has come to realize it knows almost nothing about humans and what really drives us, and it is now bringing in consultants to do what small data researchers have been doing for decades.

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The Organized Mind: Thinking Straight in the Age of Information Overload by Daniel J. Levitin

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The results of Harvard’s salary survey are no doubt intended to lead the average person to infer that a Harvard education is responsible for the high salaries of recent graduates. This may be the case, but it’s also possible that the kinds of people who go to Harvard in the first place come from wealthy and supportive families and therefore might have been likely to obtain higher-paying jobs regardless of where they went to college. Childhood socioeconomic status has been shown to be a major quantity correlated with adult salaries. Correlation is not causation. Proving causation requires carefully controlled scientific experiments. Then there are truly spurious correlations—odd pairings of facts that have no relationship to each other and no third factor x linking them. For example, we could plot the relationship between the global average temperature over the past four hundred years and the number of pirates in the world and conclude that the drop in the number of pirates is caused by global warming.

The Gricean maxim of relevance implies that no one would construct such a graph (below) unless they felt these two were related, but this is where critical thinking comes in. The graph shows that they are correlated, but not that one causes the other. You could spin an ad hoc theory—pirates can’t stand heat, and so, as the oceans became warmer, they sought other employment. Examples such as this demonstrate the folly of failing to separate correlation from causation. It is easy to confuse cause and effect when encountering correlations. There is often that third factor x that ties together correlative observations. In the case of the decline in pirates being related to the increase in global warming, factor x might plausibly be claimed to be industrialization. With industrialization came air travel and air cargo; larger, better fortified ships; and improved security and policing practices.

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Site Reliability Engineering by Betsy Beyer, Chris Jones, Jennifer Petoff, Niall Richard Murphy

Fixing the first and second common pitfalls is a matter of learning the system in question and becoming experienced with the common patterns used in distributed systems. The third trap is a set of logical fallacies that can be avoided by remembering that not all failures are equally probable—as doctors are taught, “when you hear hoofbeats, think of horses not zebras.”4 Also remember that, all things being equal, we should prefer simpler explanations.5 Finally, we should remember that correlation is not causation:6 some correlated events, say packet loss within a cluster and failed hard drives in the cluster, share common causes—in this case, a power outage, though network failure clearly doesn’t cause the hard drive failures nor vice versa. Even worse, as systems grow in size and complexity and as more metrics are monitored, it’s inevitable that there will be events that happen to correlate well with other events, purely by coincidence.7 Understanding failures in our reasoning process is the first step to avoiding them and becoming more effective in solving problems.

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Data Scientists at Work by Sebastian Gutierrez

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That’s why Tukey was writing about it in 1962 when he was ordering everybody to reorient statistics as a professional discipline and a funding line for the NSF organized around computation and data and data analysis. He wrote an article in 1962 called “The Future of Data Analysis.”13 And he wasn’t the last, right? 10 http://vserver1.cscs.lsa.umich.edu/~crshalizi/reviews/fragile-objects/ Wright, Sewall.“Correlation and causation.” Journal of Agricultural Research 20.7 (1921), 557-585. 12 Herbert Robbins and Sutton Monro, “A Stochastic Approximation Method”: Ann. Math. Statist.,Volume 22, Number 3 (1951), 400-407. 13 John W. Tukey, “The Future of Data Analysis”: Ann. Math. Statist.,Volume 33, Number1 (1962),1-67. 11 www.it-ebooks.info Data Scientists at Work Leo Breiman all throughout the 1990s was writing to his community of statisticians, “Let us get with data, statistics community!”

There are lots of challenges working with data in the algorithmic world. One of them is that thousands and thousands of other people are looking at the exact same data sets, and basically they’re all just squeezing everything they possibly can out of it. Another challenge is that people under pressure to find patterns are prone to fall into the common human fallacies of overfitting models with insufficient data and overreading correlation as causation. Gutierrez: How do the data challenges you faced in the algorithmic trading world compare to the data challenges you face at PlaceIQ? Lenaghan: The initial data challenge when I came to PlaceIQ was that geospatial data was a data type that I had never worked with. The second challenge was that the data volume was scaled up by a couple of orders of magnitude. The volume of data in the algorithmic trading I was doing was quite large—say, a terabyte a year.

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The Tyranny of Experts: Economists, Dictators, and the Forgotten Rights of the Poor by William Easterly

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42 As already mentioned, this survey does not meet the standards of hard evidence (which does not exist on the autocracy-versus-freedom issue either way). The survey does provide a rare opportunity for poor people to speak for themselves. The authors of the survey found a lot of poor people who contradicted the common assumption that poor people don’t care about their rights and care only about their material needs. EVIDENCE AND DEBATE The patterns discussed here do not prove that autocracy and collectivist values cause poverty—correlation is not causation. It could be that people who get rich for some other reason desire more individualism and democracy and are able to get it. Some studies cited here use some formal statistical methods to argue that a history of autocracy causes collectivist values, and both autocracy and collectivist values in turn cause poverty, but most economists find the methods used not very convincing. Some who favor technocratic approaches disqualify any discussion of rights because the evidence for positive consequences of rights is not rigorous enough.

And we also saw already what we see more evidence for here: a history of autocracy and violence breeds more lack of trust. The slave trade’s disastrous effects help explain a result that Nathan Nunn had already found in his doctoral dissertation—that among today’s African nations, those where Europeans had seized the most slaves were poorer than nations that had largely escaped slavery. Benin today is one of the poorest African nations.13 EVIDENCE WITH A CAUSE But once again correlation is not causation. It is plausible that the correlation could also run in reverse: poverty caused enslavement. Poorer people are less able to defend themselves because they cannot afford as many weapons as richer people. Also pre-existing lack of trust could have caused more enslavement. People who were already less trusting and less trustworthy are more likely to help the slavers by betraying their neighbors.

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The Googlization of Everything: by Siva Vaidhyanathan

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And Google was right.40 Needless to say, Anderson’s techno-fundamentalist hyperbole belies a vested interest in the narrative of the revolutionary and transformational power of computing. But here Anderson has stepped out even beyond the pop sociology and economics that usually dominate the magazine. Anderson claims “correlation is enough.”41 In other words, the entire process of generating scientiﬁc (or, for that matter, social-scientiﬁc) theories and modestly limiting claims to correlation without causation is obsolete and quaint: given enough data and enough computing power, you can draw strong enough correlations to claim with conﬁdence that what you have discovered is indisputably true. THE GOOGL I ZAT I ON OF ME MORY 197 The risk here is more than one of intellectual hubris: the academy has no dearth of that. Given the passionate promotion of such computational models for science of all types, we run the risk of diverting precious research funding and initiatives away from the hard, expensive, painstaking laboratory science that has worked so brilliantly for three centuries.

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The Vegetarian Myth: Food, Justice, and Sustainability by Lierre Keith

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They may suggest intriguing areas for exploration but until all the variables are controlled and the results are reproducible, no conclusions can be drawn. The kind of cross-country comparison that Keys did “involves comparing apples with oranges—that is countries with widely varying cultural, social, political and physical environments.”52 With such an infinite number of variables, a finding of definitive causation would be ridiculous. John Yudkin’s 1957 study shows the error of conflating correlation with causation. You can see from Figure 4B (page over) that 163 Nutritional Vegetarians 8 US 7 Canada Australia CHD, Deaths per 1000 6 5 England and Wales 4 3 2 Italy 1 0 Japan 0 10 20 30 40 50 Percent Calories from Fat Figure 4A. Correlation between the total fat consumption as a percent of total calorie consumption, and mortality from coronary heart disease in six countries. Redrawn from The Cholesterol Myths by Uffe Ravnskov. owning a TV and radio had a much stronger association with Coronary Heart Disease (CHD) than any nutritional elements.53 But no one would suggest that TV causes CHD, or that sacrificing our TVs will grant us a longer life.

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Or you could get really serious, and start to manipulate the statistics. For two pages only, this book will now get quite nerdy. I understand if you want to skip it, but know that it is here for the doctors who bought the book to laugh at homeopaths. Here are the classic tricks to play in your statistical analysis to make sure your trial has a positive result. Ignore the protocol entirely Always assume that any correlation proves causation. Throw all your data into a spreadsheet programme and report—as significant—any relationship between anything and everything if it helps your case. If you measure enough, some things are bound to be positive just by sheer luck. Play with the baseline Sometimes, when you start a trial, quite by chance the treatment group is already doing better than the placebo group. If so, then leave it like that.

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Free Ride by Robert Levine

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Culture & Empire: Digital Revolution by Pieter Hintjens

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And yet, when people stopped talking about religions, and instead looked at the politics, we found solutions. This is a consistent pattern. Conflict is always political, yet leaders often invoke religion to bolster their followers, and create more tribalism. Outsiders, searching for simplistic explanations, and possibly arms sales, embrace this rhetoric as reality. As the conflict increases, the religious arguments will definitely increase. However, it's correlation, not causation. And in the end, the solution comes from addressing the original political issues. Until then, and as long as possible, the beneficiaries (war can be incredibly profitable!) will pump up the "irreconcilable ancient hatreds" angle. And so it goes with the Global Extremist Islamic Threat to Modern Civilization. It appeals to atheists and Christians alike, and provides convenient cover, both for unprecedented profit-taking, and for creating the spy networks.

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Made to Stick: Why Some Ideas Survive and Others Die by Chip Heath, Dan Heath

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Marshall and Warren could not even get their research paper accepted by a medical journal. When Marshall presented their findings at a professional conference, the scientists snickered. One of the researchers who heard one of his presentations commented that he “simply didn’t have the demeanor of a scientist.” To be fair to the skeptics, they had a reasonable argument: Marshall and Warren’s evidence was based on correlation, not causation. Almost all of the ulcer patients seemed to have H. pylori. Unfortunately, there were also people who had H. pylori but no ulcer. And, as for proving causation, the researchers couldn’t very well dose a bunch of innocent people with bacteria to see whether they sprouted ulcers. By 1984, Marshall’s patience had run out. One morning he skipped breakfast and asked his colleagues to meet him in the lab.

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In Defense of Global Capitalism by Johan Norberg

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Criticism has been leveled at this type of regression analysis, which is based on statistics from many economies and tries to control for other factors that can affect economic outcomes, because of the many problems of measurement that such analysis involves. Coping with enormous masses of data is always a problem. Where exactly is the line between open and closed economies? How does one distinguish between correlation and causation? How can the direction of causation be established? Consider, after all, that it is common for countries implementing free trade to also introduce other liberal reforms, such as protection for property rights, reduced inflation, and balanced budgets. That makes it hard to separate the effects of one policy from the effects of another.8 The problems of measurement are real ones, and results of this kind always have to be taken with a grain of salt, but it remains interesting that, with so very few exceptions, those studies point to great advantages with free trade.

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The Driver in the Driverless Car: How Our Technology Choices Will Create the Future by Vivek Wadhwa, Alex Salkever

In February 2015, researchers from M.I.T. and from Harvard University released the results of the most comprehensive longitudinal study yet of how the diversity and types of gut flora affect onset of this type of diabetes.3 The scientists tracked what happened to the gut bacteria of a large number of subjects from birth to their third year in life, and found that children who became diabetic suffered a 25 percent reduction in their gut bacteria’s diversity. What’s more, the mix of bacteria shifted away from types known to promote health toward types known to promote inflammation. Correlation is not causation, but the results added to evidence that the bacteria in our intestines have a strong effect on our health. In fact, manipulating the microbiome may even become more important than genomics and gene-based medicine. Unlike genomics and gene therapy, which require a relatively heroic effort to induce physiological changes, tweaking the microbiome appears to be relatively straightforward and safe: just mix up a cocktail of the appropriate bacteria, and transplant it into your gut.

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But for the purposes of this chapter, FDR’s dollar meddling requires discussion, because one of the most common objections to the Federal Reserve is that since its creation in 1913, the dollar has lost more than 90 percent of its value. It’s a horrid number, and the unseen is the massive economic advances that would have made the abundant present seem impoverished by comparison but that did not come into being. However, this objection to the Fed is one of those instances where correlation is not causation. Lest we forget, FDR decided to devalue the dollar, and per Shlaes, “It did not matter what the Federal Reserve said.” Stated simply, the first major decline in the value of the dollar had nothing to do with the Fed. So incensed was Fed Chairman Eugene Meyer by FDR’s decision that he actually resigned.6 Let’s shift to 1944 and the Bretton Woods monetary conference at the Mount Washington Hotel.

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10% Human: How Your Body's Microbes Hold the Key to Health and Happiness by Alanna Collen

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The data included information about antibiotic use when the children were infants. It turned out that those who had been given antibiotics before the age of two – a startling 74 per cent of them – were on average nearly twice as likely to have developed asthma by the time they were eight. The more courses of antibiotics the children received, the more likely they were to develop asthma, eczema and hay fever. But, as the saying goes, correlation does not always mean causation. The lead researcher on the antibiotics study had discovered four years earlier that the more television children watched, the more likely they were to develop asthma. Of course, despite similar results as in the antibiotics study, no one really believed that the act of watching television could bring about immune dysfunction in the lungs. In fact, the number of hours in front of a television was being used as a proxy for the amount of exercise children were getting.

It took time for antibiotics to reach common usage, for further antibiotic drugs to be developed, for children to grow up with the influence of these drugs on their bodies, and for chronic diseases to develop in their own insidious way. It also takes time for the effects to become clear across populations, countries and continents. If the introduction of antibiotics in 1944 is in some way responsible for our current state of health, the 1950s are exactly when we would expect to see the dawning of their impact. Let us not jump the gun though. As any scientist would hasten to point out, correlation does not always mean causation. The timely introduction of antibiotics may be as unrealistic a connection to rising chronic illness as the self-serve supermarkets that made their debut in the 1940s. Connections alone, whilst useful guides, do not always provide a causal link. An amusing website about spurious correlations tells me that there’s an impressively close correlation between per capita consumption of cheese in the US and the number of people who die each year by becoming tangled in their bed sheets.

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Republic, Lost: How Money Corrupts Congress--And a Plan to Stop It by Lawrence Lessig

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As Fiorina and Abrams put it, “the natural place to look for campaign money is in the ranks of the single-issue groups, and a natural strategy to motivate their members is to exaggerate the threats their enemies pose.”29 In this odd and certainly unintended way, then, the demand for cash could also be changing the substance of American politics. Could be, because all I’ve described is correlation, not causation. But at a minimum the correlation should concern us: On some issues, the parties become more united—those issues that appeal to corporate America. On other issues, the parties become more divided—the more campaign funds an issue inspires, the more extremely it gets framed. In both cases, the change correlates with a strategy designed to maximize campaign cash, while weakening the connection between what Congress does (or at least campaigns on) and the potential needs of ordinary Americans.

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The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World by Pedro Domingos

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This technique, called A/B testing, was at first used mainly in drug trials but has since spread to many fields where data can be gathered on demand, from marketing to foreign aid. It can also be generalized to try many combinations of changes at once, without losing track of which changes lead to which gains (or losses). Companies like Amazon and Google swear by it; you’ve probably participated in thousands of A/B tests without realizing it. A/B testing gives the lie to the oft-heard criticism that big data is only good for finding correlations, not causation. Philosophical fine points aside, learning causality is learning the effects of your actions, and anyone with a stream of data they can affect can do it—from a one-year-old splashing around in the bathtub to a president campaigning for reelection. Learning to relate If we endow Robby the robot with all the learning abilities we’ve seen so far in this book, he’ll be pretty smart but still a bit autistic.

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Come as You Are: The Surprising New Science That Will Transform Your Sex Life by Emily Nagoski Ph.d.

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Suppose you recognize that nonconcordance exists, you acknowledge that it’s expecting without necessarily indicating enjoying or eagerness, and then you read the research that shows there is a correlation between nonconcordance and sexual dysfunctions related to desire and arousal.21 And so you decide that, because nonconcordance is associated with dysfunction, nonconcordance must be a problem. Which brings me to a sentence every undergraduate who takes a research methods class will memorize: “Correlation does not imply causation.” It refers to the cum hoc ergo propter hoc fallacy—“with this, therefore because of this”—which means that just because two things happen together doesn’t mean that one thing caused the other thing. The quintessential example in the twenty-first century is the relationship between pirates and global warming.22 This is a joke made by Bobby Henderson, as part of the belief system of the Church of the Flying Spaghetti Monster.

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Machine Learning for Email by Drew Conway, John Myles White

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pages: 187 words: 55,801

The New Division of Labor: How Computers Are Creating the Next Job Market by Frank Levy, Richard J. Murnane

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pages: 901 words: 234,905

The Blank Slate: The Modern Denial of Human Nature by Steven Pinker

Equally surprising are the sorry standards to which the great scholar here has sunk. The suggestion that a language can be “grown-up” and “masculine” is so subjective as to be meaningless. He attributes a personality trait to an entire people without any evidence, then advances two theories—that phonology reflects personality, and that warm climates breed laziness—without invoking even correlational data, let alone proof of causation. Even on his home ground the reasoning is flimsy. Languages with a consonant-vowel syllable structure like Hawaiian call for longer words to convey the same amount of information, hardly what you would expect in a people without “vigor or energy.” And the consonant-encrusted syllables of English are liable to be swallowed and misheard, hardly what you would expect from a logical, businesslike people.

The parents of an affectionate child may return that affection and thereby act differently from the parents of a child who squirms and wipes off his parents’ kisses. The parents of a quiet, spacey child might feel they are talking to a wall and jabber at him less. The parents of a docile child can get away with setting firm but reasonable limits; the parents of a hellion might find themselves at their wits’ end and either lay down the law or give up. In other words, correlation does not imply causation. A correlation between parents and children does not mean that parents affect children; it could mean that children affect parents, that genes affect both parents and children, or both. It gets worse. In many studies, the same parties (in some studies the parents, in others the children) supply the data on both the parents’ behavior and the child’s. Parents tell the experimenter how they treat their children and what their children are like, or adolescents tell the experimenter what they are like and how their parents treat them.

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The Skeptical Economist: Revealing the Ethics Inside Economics by Jonathan Aldred

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And if it can, is that what these surveys are measuring? With its breezy optimism the happiness experts’ reading of the evidence ignores some awkward objections. To begin with, it is easy to pick holes in the neuroscience. As ever, the problem is not the science itself, but the interpretative spin put on the results. To begin with, the core of the neuroscientific research is a set of correlations which do not demonstrate any causation. There is little understanding of why external stimuli are associated with increased brain activity, so there is no basis for assuming causation. And even the correlations are less robust than they appear, because of the assumptions which have been made to derive them. For instance, most of the research adopts the ‘subtractive method’, in which measurements of brain activity in the control condition (when there is no stimulus) are subtracted from measurements in the experimental condition (when the stimulus is present).

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Rethinking the Economics of Land and Housing by Josh Ryan-Collins, Toby Lloyd, Laurie Macfarlane, John Muellbauer

Without the existence of a credit- and money-creating banking system, it is impossible to envisage how such huge increases in prices would have been possible given the slower pace of income growth. The relationship between mortgage credit and house prices also applies internationally. An International Monetary Fund (IMF) study of thirty-six advanced and emerging economies (including the UK) found that a 10 percentage point growth in mortgage credit as a percentage of GDP was associated with a 16 percentage point higher growth of real house prices (IMF, 2011). Of course, correlation is not causation and it is likely that rising house prices, potentially driven by other factors, lead to an increase in demand for mortgage credit which itself helps to drive up house prices (Goodhart and Hofmann, 2008). Figure 5.3 Disaggregated nominal credit stocks (loans outstanding) as % of GDP in the UK since 1963 (source: Bank of England, GDP from ONS; credit series are break adjusted) Figure 5.3 shows how, since the early 1980s, UK banks have significantly increased their lending to domestic mortgages relative to GDP.

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The Cost of Inequality: Why Economic Equality Is Essential for Recovery by Stewart Lansley

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‘I could hardly believe how tight the fit was—it was a stunning correlation,’ Moss told the New York Times. ‘And it began to raise the question of whether there are causal links between financial deregulation, economic inequality and instability.’240 Of course, as Moss has accepted, correlation is not the same as causation. As one of his critics, R Glenn Hubbard, dean of the Columbian Business School and top economic adviser to former President George W Bush has put it, ‘Cars go faster every year, and GDP rises every year, but that doesn’t mean speed causes GDP.’ 241 The correlation could mean that the direction of causation is from slump to inequality. Yet what is significant about this pattern is that in both the 1920s and the pre-2007 period, inequality rose sharply in the years before recession took hold. There is now an increasing, if still small, body of academics that have attributed the crisis at least in part to rising inequality.

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Surviving AI: The Promise and Peril of Artificial Intelligence by Calum Chace

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The book points out some interesting unexpected side-effects of Big Data. It turns out that having more data beats having better data, if what you want is to be able to understand, predict and influence the behaviour of large numbers of people. It also turns out that if you find a reliable correlation then it often doesn’t matter if there is a causal link between the two phenomena. We all know of cases where correlation has been mistaken for causation and ineffective or counter-productive policies have been imposed as a result. But if a correlation persists long enough it may provide decision-makers with a useful early warning signal. For instance, a supermarket and an insurance company shared data sets and discovered that men buying red meat and milk during the day were better insurance risks than men buying pasta and petrol late at night.

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Collapse: How Societies Choose to Fail or Succeed by Jared Diamond

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Among McGovern’s papers are Thomas McGovern, “The Vinland adventure: a North Atlantic perspective” (North American Archaeologist 2:285-308 (1981)); Thomas McGovern, “Contributions to the paleoeconomy of Norse Greenland” (Acta Archaeologica 54:73-122 (1985)); Thomas McGovern et al., “Northern islands, human era, and environmental degradation: a view of social and ecological change in the medieval North Atlantic” (Human Ecology 16:225-270 (1988)); Thomas McGovern, “Climate, correlation, and causation in Norse Greenland” (Arctic Anthropology 28:77-100 (1991)); Thomas McGovern et al., “A vertebrate zooarchaeology of Sandnes V51: economic change at a chieftain’s farm in West Greenland” (Arctic Anthropology 33:94-121 (1996)); Thomas Amorosi et al., “Raiding the landscape: human impact from the Scandinavian North Atlantic” (Human Ecology 25:491-518 (1997)); and Tom Amorosi et al., “They did not live by grass alone: the politics and paleoecology of animal fodder in the North Atlantic region” (Environmental Archaeology 1:41-54 (1998)).

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Collapse by Jared Diamond

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Among McGovern's papers are Thomas McGovern, "The Vinland adventure: a North Atlantic perspective" (North American Archaeologist 2:285-308 (1981)); Thomas McGovern, "Contributions to the paleoeconomy of Norse Greenland" (Acta Archaeologica 54:73-122 (1985)); Thomas McGovern et al., "Northern islands, human era, and environmental degradation: a view of social and ecological change in the medieval North Atlantic" (Human Ecology 16:225-270 (1988)); Thomas McGovern, "Climate, correlation, and causation in Norse Greenland" (Arctic Anthropology 28:77-100 (1991)); Thomas McGovern et al., "A vertebrate zooarchaeology of Sandnes V51: economic change at a chieftain's farm in West Greenland" (Arctic Anthropology 33:94-121 (1996)); Thomas Amorosi et al, "Raiding the landscape: human impact from the Scandinavian North Atlantic" (Human Ecology 25:491-518 (1997)); and Tom Amorosi et al, "They did not live by grass alone: the politics and paleoecology of animal fodder in the North Atlantic region" (Environmental Archaeology 1:41-54 (1998)).

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Stress Test: Reflections on Financial Crises by Timothy F. Geithner

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I helped call more attention to the vulnerabilities that come from risky forms of financing, alongside the IMF’s traditional focus on large fiscal deficits and inflation. And while the Bush team liked to criticize the bailouts of the Clinton era, they ultimately supported large IMF rescue packages for Brazil, Uruguay, and Turkey with the familiar wall-of-money strategy. That was what the IMF was for. Years later, Mervyn King, the governor of the Bank of England, joked at a farewell dinner that I was a textbook proof of the difference between correlation and causation. “Tim was present at all the crises,” he said. “But he didn’t cause the crises. The crises caused him.” Again and again, I got to see how indulgent capital financed booms, how cracks in confidence turned boom to bust to panic, how crisis managers could help contain panics with decisiveness and overwhelming force, and how the kind of actions needed to defuse crises were inherently unpopular and fraught with risk.

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Capitalism 4.0: The Birth of a New Economy in the Aftermath of Crisis by Anatole Kaletsky

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The question therefore arises whether Japan is now the most plausible model for a New Normal of sluggish growth and financial paralysis in the United States, Britain, and other economies emerging from the credit crunch. Luckily, this analogy between Japan and the Western world looks increasingly far-fetched. It is certainly true that the Japanese financial system remained paralyzed for a decade as banks and borrowers survived on government life support and failed to recognize their true losses. It is true also that the Japanese economy spent twenty years almost continuously in recession. But correlation is not causation. The question that needs to be asked about the Japanese experience is whether government support for struggling banks and overindebted borrowers caused the twenty years of stagnation or whether twenty years of economic stagnation prevented a recovery for weak borrowers and banks. A similar question must be asked about a fascinating and much-quoted historic study, coauthored by Carmen Reinhart and Kenneth Rogoff, the IMF’s former chief economist, which looked at the macroeconomic effect of financial crises in dozens of countries over the past six hundred years.

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David Mitchell: Back Story by David Mitchell

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Although it may explain some of the murders (see Book 2). I’m always suspicious of that ‘comedy comes from pain’ reasoning. Trite magazine interviewers talk to comedians, tease a perfectly standard amount of doubt, fear and self-analysis out of them and infer therefrom that it’s this phenomenon of not-feeling-perpetually-fine that allowed them to come up with that amusing routine about towels. Well, correlation is not causation, as they say on Radio 4’s statistics programme More or Less. Everyone’s unhappy sometimes, and not everyone is funny. The interviewers may as well infer that the comedy comes from the inhalation of oxygen. Which of course it partly does. We have no evidence for any joke ever having emanated from a non-oxygen-breathing organism. At a sub-atomic level, oxygen is absolutely packed with hilarions.

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The Patient Will See You Now: The Future of Medicine Is in Your Hands by Eric Topol

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Later, a team of four highly respected data scientists wrote in Science that GFT had systematically overestimated the prevalence of flu every week since August 2011, going on to criticize “big data hubris,” the “often implicit assumption that big data are a substitute for, rather than a supplement to, traditional data collection and analysis.”17 They attacked the “algorithm dynamics” of GFT, pointing out that the forty-five search terms used were never documented, key elements such as core search terms were not provided in the publications, and the original algorithm did not undergo constant adjustment and recalibration. What’s more, while the GFT algorithm was static, the search engine itself underwent constant change—as many as six hundred revisions per year—which was not taken into account. Many other editorialists opined on the matter.13–15,18,19 Correlation rather than causation and the critical absence of context were the most prominent critique points. There was also the sampling issue as the crowdsourcing was limited to those doing searches on Google. Further, there was a major analytical problem: GFT performed so many multiple comparisons of data that they were likely to be getting spurious results. These can all be viewed as common traps when we are trying to understand the world through data.13 As Krenchel and Madsbjerg wrote in Wired, “The real big data hubris is not that we have too much confidence in a set of algorithms and methods that aren’t quite there yet.

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Happy City: Transforming Our Lives Through Urban Design by Charles Montgomery

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And people who trust their neighbors feel a greater sense of that belonging. And that sense of belonging is influenced by social contact. And casual encounters (such as, say, the kind that might happen around a volleyball court on a Friday night) are just as important to belonging and trust as contact with family and close friends. It is hard to say which condition is lifting the others—Helliwell admits that his statistical analysis demonstrates correlation rather than causation—but what is strikingly apparent is that trust, feelings of belonging, social time, and happiness are like balloons tied together in a bouquet. They rise and fall together. This suggests that it has been a terrible mistake to design cities around the nuclear family at the expense of other ties. But it also suggests that even the high-status, deeply desired, uniquely biophilic brand of verticalism embodied by Vancouverism and McDowell’s high-rise apartment is not a panacea.

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Ghettoside: A True Story of Murder in America by Jill Leovy

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The tables I’ve compiled include names of victims, circumstances of deaths, and, in many cases, observations made at crime scenes and funerals and information provided by families and detectives. Over the years, in search of clarity on clearance rates, I have conducted surveys of case outcomes by calling or visiting the assigned detectives or their field supervisors and asking for updates. For years now, I have tried to penetrate the mystery of disproportionate black homicide. Correlation is not causation. I wanted to know exactly what was happening and why. I’ve sought answers in reported facts and observations, and tried to avoid pat speculation and received wisdom. Mostly, I’ve relied on what I have myself seen or heard directly from those who are close to homicide. I have made deliberate efforts to listen to the bereaved—to seek out the parents, siblings, spouses, and children of black homicide victims, whose viewpoints are under-represented in our national debates over criminal justice.

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Cooked: A Natural History of Transformation by Michael Pollan

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., “Ingestion of Lactobacillus Strain Regulates Emotional Behavior and Central GABA Receptor Expression in a Mouse via the Vagus Nerve,” Proceedings of the National Academy of Sciences 108 No. 38 [2011]: 16050–55). * It has long been recognized that people with autism and schizophrenia often suffer from gastrointestinal disorders, and some recent work suggests there may be anomalies in their microflora. It’s important to remember that correlation is not causation, and if there is causation, we don’t know which way it goes. But evidence is accumulating that certain microbes in our bodies can affect our behavior and do so for their own purposes. Toxoplasma gondii, a parasite found in more than one billion people worldwide, has been shown to inspire neurotic self-destructive behavior in rats. The protozoa’s reproductive cycle depends on infecting cats, which it does by getting them to eat the rats and mice in whose brains the parasite commonly resides.

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The Age of Em: Work, Love and Life When Robots Rule the Earth by Robin Hanson

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For example, people today tend to be both happier and more productive when they have jobs, autonomy at work, health, beauty, money, marriage, religion, intelligence, extroversion, conscientiousness, agreeableness, and non-neuroticism (Myers and Diener 1995; Lykken and Tellegen 1996; Steen 1996; Nguyen et al. 2003; Barrick 2005; Roberts et al. 2007; Sutin et al. 2009; Erdogan et al. 2012; Diener 2013; Ali et al. 2013; Stutzer and Frey 2013). Of course correlation isn’t causation, and there is much we don’t understand here. Even so, the consistency of the relationship between happiness and productivity gives us much reason to hope that more productive ems may on average be happier than people today. Yes, perhaps work productivity makes people happier by raising their relative status, and by definition relative status can’t rise for everyone. But even in that case, relative status can’t fall overall either, to hurt overall happiness.

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I Think You'll Find It's a Bit More Complicated Than That by Ben Goldacre

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Systematic reviews of randomised trials are considered to be the most reliable: because they ensure that your conclusions are based on all of the information, rather than just some of it; and because randomised trials – when conducted properly – are the least vulnerable to bias, and so they are the ‘most fair tests’. After these, there are observational studies: these are much more prone to bias, and produce findings which might just reflect correlation instead of causation (‘People who choose to eat vegetables live longer’) but they are generally cheaper to do. Then there are individual case reports. And then, finally, because medical academics like to think they’re funny, right at the bottom of the hierarchy you will find something called ‘expert opinion’. In the Dartmouth study, among the press releases covering human research, only 17 per cent promoted the studies with the strongest designs, either randomised trials or meta-analyses.

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The Economics of Enough: How to Run the Economy as if the Future Matters by Diane Coyle

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For example, one paper reports rising happiness in forty-five of fifty-two countries for which times-series data are available (for the years 1981–2007) and links it to rising freedom and economic development.34 Another confirms that income, alongside social indicators, explains much of the difference in self-reported happiness levels within countries and between countries.35 This seems a much more credible result than the original Easterlin Paradox. But the temptation to read too much into this should be resisted. This is partly for reasons of sensible caution about the statistics. All of this work looks at statistical correlations and not at causation. Happier people might be more productive, leading to higher growth and incomes, rather than the causality running the other way. Alternatively, other factors that do cause happiness might be linked in turn to growth—such as better health or greater access to education—making the observed correlation between happiness and growth an indirect one. Moreover, there is other evidence on the relationship between economic and social measures and happiness that gives useful insights when it comes to policy.

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The Ghost Map: A Street, an Epidemic and the Hidden Power of Urban Networks. by Steven Johnson

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Farr thought that the single most reliable predictor of environmental contamination was elevation: the population living in the putrid fog that hung along the riverbanks were more likely to be seized by the cholera than those living in the rarefied air of, say, Hampstead. And so, after the 1849 outbreak, Farr began tabulating cholera deaths by elevation, and indeed the numbers seemed to show that higher ground was safer ground. This would prove to be a classic case of correlation being mistaken for causation: the communities at the higher elevations tended to be less densely settled than the crowded streets around the Thames, and their distance from the river made them less likely to drink its contaminated water. Higher elevations were safer, but not because they were free of miasma. They were safer because they tended to have cleaner water. Farr was not entirely opposed to Snow’s theory.

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Hidden Family by Stross, Charles

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He’d understand: That was half the attraction that had sparked their whirlwind affair. He probably grasped the headaches she was facing better than anyone else, Brill included. Brill was still not much more than a teenager with a sheltered upbringing. But Roland knew just how nasty things could get. If I trust him, she thought wistfully. Someone had murdered the watchman and installed the bomb in the warehouse. She’d told Roland about the place, and then … correlation does not imply causation, she told herself. In the end she compromised halfway, taking the T into town and finding a diner with a good range of exit options before switching on the phone and dialing. That way, even if someone had grabbed Roland and was actively tracing the call, they wouldn’t find her before she ended the call. It was raining, and she had a seat next to the window, watching the slug-trails of rain on the glass as her latte cooled while she tried to work up her nerve to call him.

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Hacking Growth: How Today's Fastest-Growing Companies Drive Breakout Success by Sean Ellis, Morgan Brown

The analysis of the highly retained users who came back at least seven times a month revealed that these people were generally following a number of other users that hovered around 30; some were following many more, but 30 seemed to be a “tipping point” number that hooked people to coming back for more. But Elman and the team didn’t stop there. They knew that, as the mantra in statistical testing goes, correlation does not equal causation. Now, at that point they might have started just trying to boost the number of people that users were following and left it at that. And indeed that might have produced appreciable results. But what if the sheer desire to follow a significant number of people wasn’t the underlying reason that following 30 people made the product sticky? So the growth team dug further into the data and soon found another correlation.

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Dead Aid: Why Aid Is Not Working and How There Is a Better Way for Africa by Dambisa Moyo

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Despite the widespread Western belief that ‘the rich should help the poor, and the form of this help should be aid’, the reality is that aid has helped make the poor poorer, and growth slower. In Moyo’s startling words: ‘Aid has been, and continues to be, an unmitigated political, economic, and humanitarian disaster for most parts of the developing world.’ In short, it is (as Karl Kraus said of Freudianism) ‘the disease of which it pretends to be the cure’. The correlation is certainly suggestive, even if the causation may be debated. Over the past thirty years, according to Moyo, the most aid-dependent countries have exhibited an average annual growth rate of minus 0.2 per cent. Between 1970 and 1998, when aid flows to Africa were at their peak, the poverty rate in Africa actually rose from 11 per cent to a staggering 66 per cent. Why? Moyo’s crucial insight is that the receipt of concessional (non-emergency) loans and grants has much same effect in Africa as the possession of a valuable natural resource: it’s a kind of curse because it encourages corruption and conflict, while at the same time discouraging free enterprise.

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What They Do With Your Money: How the Financial System Fails Us, and How to Fix It by Stephen Davis, Jon Lukomnik, David Pitt-Watson

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See a May 1, 2013, debate at the American Enterprise Institute at www.aei.org/events/2013/05/01/shareholder-value-theory-myth-or-motivator/. 4. Keith Ambachtsheer, Ronald Capelle, and Hubert Lum, “The Pension Governance Deficit: Still with Us” (Social Science Research Network, 2008), http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1280907. Critics rightly caution that while the study finds correlation between fund governance and performance, causation is elusive. In other words, outperformance could help produce good governance rather than the other way around. Also, the period of four years examined by the authors is relatively short given retirement timeframes. 5. To illustrate, apply the academic projection to the real world. Let’s plug in numbers for an employee at age thirty who starts with a \$50,000 a year salary and typical contributions to a 401(k).

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Is God a Mathematician? by Mario Livio

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This is an example of selection effects—biases introduced in the results due to either the apparatus used for collecting the data or the methodology used to analyze them. Sampling presents another problem. For instance, modern opinion polls usually interview no more than a few thousand people. How can the pollsters be sure that the views expressed by members of this sample correctly represent the opinions of hundreds of millions? Another point to realize is that correlation does not necessarily imply causation. The sales of new toasters may be on the rise at the same time that audiences at concerts of classical music increase, but this does not mean that the presence of a new toaster at home enhances musical appreciation. Rather, both effects may be caused by an improvement in the economy. In spite of these important caveats, statistics have become one of the most effective instruments in modern society, literally putting the “science” into the social sciences.

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Why People Believe Weird Things: Pseudoscience, Superstition, and Other Confusions of Our Time by Michael Shermer

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Mothers who bottle-fed their babies were made to feel guilty. But soon researchers began to wonder whether breast-fed babies are attended to differently. Maybe nursing mothers spend more time with their babies and motherly vigilance was the cause behind the differences in IQ. As Hume taught us, the fact that two events follow each other in sequence does not mean they are connected causally. Correlation does not mean causation. 13. Coincidence In the paranormal world, coincidences are often seen as deeply significant. "Synchronicity" is invoked, as if some mysterious force were at work behind the scenes. But I see synchronicity as nothing more than a type of contingency—a conjuncture of two or more events without apparent design. When the connection is made in a manner that seems impossible according to our intuition of the laws of probability, we have a tendency to think something mysterious is at work.

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Power Hungry: The Myths of "Green" Energy and the Real Fuels of the Future by Robert Bryce

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As a journalist, I’m skeptical of nearly everything. When my mother told me she loved me, I doublechecked it with my dad. And now, with legions of greens, politicos, and pundits all parroting the same message about the dangers of global warming, my reflexive skepticism only increases. My skepticism about the conventional wisdom on global warming arises from two main points. First, I adhere to one of the oldest maxims in science: Correlation does not prove causation. Carbon dioxide levels in the atmosphere may be increasing, but that does not necessarily prove that the carbon dioxide is causing any warming that may be occurring. Second, models are only as good as the data going into them. All of the alarm bells now being sounded are based on atmospheric and climatic models about how temperatures in the future are expected to react, given the data fed into the models.

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Flow by Mihaly Csikszentmihalyi

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It needs to be stressed again and again that what counts is the quality of experience flow provides, and that this is more important for achieving happiness than riches or fame. At the same time, it would be disingenuous to ignore the fact that successful people tend to enjoy what they do to an unusual extent. This may indicate that people who enjoy what they are doing will do a good job of it (although, as we know, correlation does not imply causation). A long time ago, Maurice Schlick (1934) pointed out how important enjoyment was in sustaining scientific creativity. In an interesting recent study, B. Eugene Griessman interviewed a potpourri of high achievers ranging from Francis H. C. Crick, the codiscoverer of the double helix, to Hank Aaron, Julie Andrews, and Ted Turner. Fifteen of these celebrities completed a questionnaire in which they rated the importance of thirty-three personal characteristics, such as creativity, competence, and breadth of knowledge, in terms of helping them achieve success.

pages: 298 words: 43,745

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It is to be noted that the rule defines both the magnitude and the measure [8]. The metrics of reach and frequency as measurements of sponsored search effects and analysis have been used in advertising for at least twenty-five years. What should be reported, however, is effective reach. That is, to be meaningful, media reach and frequency measurements must be related to advertising communication goals. Potpourri: “Correlation does not imply causation” is a common catchphrase in empirical analysis. Its meaning is that just because two variables are correlated does not mean that one causes the other. Typically, correlation is a necessary but not sufficient condition for causation. Most advertising has an objective to capture attention and maintain awareness. Advertising analysts for this reason have measured the effect of frequency based on communication goals.

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Common Wealth: Economics for a Crowded Planet by Jeffrey Sachs

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The scatter plot of 150 countries in Figure 8.2(a) shows that lower rates of under-five mortality are associated with lower rates of total fertility. The scatter plot in Figure 8.2(b) shows that lower under-five mortality is associated with a lower overall rate of population growth, suggesting that the decline in mortality is more than offset by an accompanying decline in fertility. Correlation does not prove causation, but ample experience, and more sophisticated statistical testing, does. By saving children’s lives, and reaping the benefits in lower fertility rates, societies not only save their children but also help to stabilize their populations at the same time. Figure 8.2(a): Child Mortality and Total Fertility Rates in 2005 Source: Data from World Bank (2007) Figure 8.2(b): Population Growth and Child Mortality Rates in 2005 Source: Data from World Bank (2007) Education of Girls Girls’ education has time and again been shown to be one of the decisive entry points into the demographic transition.

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The Science of Fear: How the Culture of Fear Manipulates Your Brain by Daniel Gardner

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When we examine the statistics and decide that the odds of being killed in a terrorist attack are far too small to worry about, Head is doing the work. Head is our best bet for accurate results, but it has limitations. First, Head needs to be educated. We live in a world of complex information, and if Head doesn’t learn the basics of math, stats, and logic—if it doesn’t know the difference between an increaseof 5 percent and an increase of 5 percentage points, say, or that correlation does not prove causation—it can make bad mistakes. Head also works very slowly. That may not be a problem when you are reading the newspaper at the breakfast table, but it’s a little troublesome when you see a shadow move in long grass and you have to decide what to do without consulting an encyclopedia to determine the prevalence and hunting habits of lions. System One, or Gut, is unconscious thought, and its defining quality is speed.

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Our Kids: The American Dream in Crisis by Robert D. Putnam

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Children born in 1990 to high school dropouts were more than four times as likely to have a parent sent to prison as were children born that same year to college-educated parents. More than half of all black children born to less educated parents in 1990 experienced parental imprisonment.57 This period of exploding incarceration is precisely the period in which single-parent families became more and more common in the less educated, lower-income stratum of the population. Correlation does not prove causation, of course, but mass incarceration has certainly removed a very large number of young fathers from poor neighborhoods, and the effects of their absence, on white and nonwhite kids alike, are known to be traumatic, leaving long-lasting scars. They certainly did in David’s life in Ohio and Joe’s life in Oregon. Paternal incarceration (independent of other facts about a child’s background, like the parents’ education and income and race) is a strong predictor of bad educational outcomes, like getting poor grades and dropping out of school.

Jennifer Morgue by Stross, Charles

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"What" "Primus, we're destiny-entangled. I can't do anything about that. You stub your toe, I hurt'; I call you names, you get pissy. But you're making a big mistake. Because, secundus, you had a weird dream. And you're jumping to the conclusion that the two are related, that whatever you dreamed about is whatever happened to me. And you know what? That ain't necessarily so. Correlation does not imply causation. Now — " she reaches over and pokes me in the chest with a fingertip " — you seem a little upset over whatever it was you dreamed about. And I think you ought to think very hard before you ask the next question, because you can choose to ask whether there was any connection between your weird dream and my night out — or you can just tell yourself you ate too many cheese canapes before bed and it was all in your head, and you can walk away from it.

Culture and Prosperity: The Truth About Markets - Why Some Nations Are Rich but Most Remain Poor by John Kay

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24 Openness-productive countries have fewer restrictions on trade with other countries. 25 Population growth is lower in productive economies. 26 Property rights are more secure in rich states. 27 Religion-from the standpoint of earthly productivity, it is better to live in a society whose traditions are Christian, and among Christians, it is better to live in a predominantly Protestant tradition than in a mainly Catholic one. 28 Tolerance-more people in rich states answer yes to questions like "Should people be allowed to live as they choose?" 29 Correlation does not imply causation. Average height is greater in rich states. Are tall people more productive than short people? Or does higher productivity make people taller? I doubt if either of these things is true. The most likely explanation is that higher standards ofliving, which result from higher productivity, lead to better nutrition. In turn, better nutrition leads to greater adult height and still higher productivity.

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Inside the Nudge Unit: How Small Changes Can Make a Big Difference by David Halpern

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Even a cursory glance at the relationship between levels of subjective well-being and income strongly suggests that money does buy at least some happiness.9 The correlation between the two, at national level, is around 0.8 – or about as strong a relationship as is found in the social sciences. A similar shaped curvilinear relationship is found within countries, with the rich consistently reporting greater life satisfaction than the poor. Of course, correlation does not imply causation. It is likely that at least some of this relationship is mediated by other factors, such as better healthcare and education in richer countries or places. It is even plausible that some of the differences in income are partly driven by well-being, rather than the other way around, at least within some populations. It is much less likely that individual level differences in outlook, or positive psychology, can explain national differences in GDP, though some have suggested it.10 There’s more to material and environmental factors than income, of course.

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Selfie: How We Became So Self-Obsessed and What It's Doing to Us by Will Storr

Does the involvement in alcohol for years or decades constitute the causal basis for the feelings of worthlessness that we discovered in people who have been involved in that?’ This was the crux. Vasco had promised the legislature the data showed the ‘causative’ effects of low self-esteem. And this was precisely what Professor Smelser was saying was not there. Correlative findings can be as good as useless; as every science student knows, correlation does not equal causation. You might find domestic violence to be correlated with liking Dolly Parton (you might find that, I have no idea) but that doesn’t mean Dolly Parton is the cause of domestic violence or, indeed, that eradicating Dolly Parton would be a vaccine for it. At the end of his presentation, Smelser gave the task force a warning. The data, he said, was not going to give them something they could ‘hand on a platter to the legislature and say, “This is what you’ve got to do and you’re going to expect the following kind of results.”

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Tools of Titans: The Tactics, Routines, and Habits of Billionaires, Icons, and World-Class Performers by Timothy Ferriss

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I see, hear, feel, and know that the purpose of my life is to inspire and guide the transformation of humanity on and off the Earth.” Peter’s breathing is similar to some of Wim Hof’s exercises (page 41), which I now do in a cold shower (state “priming” per Tony Robbins, page 210), right after my morning meditation. As for the flossing-longevity connection, Peter is the first to admit this might be correlation instead of causation: People anal retentive enough to floss regularly probably have other habits that directly contribute to longer life. Pre-Bed Routines Before bed, Peter always reviews his three “wins of the day.” This is analogous to the 5-Minute Journal p.m. review that I do (page 146). On Getting out of Funks TIM: “To get out of that 2-day funk [after one of his early startups failed], what does the self-talk look like?

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Trick or Treatment: The Undeniable Facts About Alternative Medicine by Edzard Ernst, Simon Singh

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The central problem is that we are tempted to assume that two events that happen one after the other must be connected. If recovery from illness takes place after taking some homeopathic pills, then isn’t it obvious that the homeopathic pills caused the recovery? If there is a correlation between two events, then isn’t it common sense that one event caused the other? The answer is ‘No’. We can see why a correlation should not be confused with causation if we look at a neat example invented by Bobby Henderson, author of The Gospel of the Flying Spaghetti Monster. He spotted a very interesting correlation between the increase in global temperature over the last two centuries and the decline in the number of pirates. If correlation is synonymous with cause and effect, then he speculated that the decline in pirates is causing global warming.

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Adapt: Why Success Always Starts With Failure by Tim Harford

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Sometimes there is no choice but to perform an experiment yourself. Even with better data, the truth is not always apparent. For example, Lind had speculated that scurvy was connected with beer, because he noticed that scurvy often struck when a ship’s supply of beer ran out. But this was coincidence: both were the result of a long voyage, but scurvy has nothing to do with a deficiency of beer. Correlation is a treacherous guide to causation. There is, naturally, an ethical question over all this. Ten of Lind’s twelve scurvy sufferers saw their illnesses deteriorate as they took salt water, sulphuric acid and various other substances that proved to be useless as cures for scurvy. When we really have no idea what the right treatment is, there is little downside here: with the possible exception of the pair taking sulphuric acid, the ten sick sailors would have been no worse off without Lind on board.

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Endless Money: The Moral Hazards of Socialism by William Baker, Addison Wiggin

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This firm’s researchers, who are tasked to make money rather than to win Nobel Prizes, would be unlikely to bet heavily on a correlation that cannot explain nearly half of a model’s outcome. Foolishly, those running the Fed would not hesitate to do so even though the wealth of America and the world is put at great risk. Moreover, the first thing statistics students are taught is that correlation does not necessarily mean causation. The stock market may rise when old NFL franchises win the Super Bowl and fall otherwise; it is inexplicable why this is so, and clearly there is no cause and effect. After having solved the riddle of the Great Depression, to leave the back door open by saying that this is so except for the experience of the 1920s and 1930s seems odd. And it is especially suspect now that in a world of freely floating currencies the freezing up of credit markets caused the stock market to crash in a mere subset of months within 2008.

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The Origins of Political Order: From Prehuman Times to the French Revolution by Francis Fukuyama

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Putting the two together makes possible mental models—that is, general statements about causation (“it gets warm because the sun shines”; “society forces girls into stereotyped gender roles”). All human beings engage in the construction of abstract mental models; our ability to theorize in this fashion gives us huge survival advantages. Despite the warnings of philosophers like David Hume and countless professors in first-year statistics classes that correlation does not imply causation, human beings are constantly observing correlations between events in the world around them and inferring causation from them. By not stepping on the snake or eating the root that killed your cousin last week, you avoid being subject to the same fate, and you can quickly communicate that rule to your offspring. The ability to create mental models and to attribute causality to invisible abstractions is in turn the basis for the emergence of religion.

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The Big Fat Surprise: Why Butter, Meat and Cheese Belong in a Healthy Diet by Nina Teicholz

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The idea that fat might lead to cancer was first aired at the McGovern committee hearings in 1976, when Gio Gori, director of the National Cancer Institute (NCI), testified that men and women in Japan had very low rates of breast and colon cancer and that those rates rose quickly upon emigrating to the United States. Gori showed charts demonstrating the parallel rising lines of fat consumption and cancer rates. “Now I want to emphasize that this is a very strong correlation, but that correlation does not mean causation,” he said. “I don’t think anybody can go out today, and say that food causes cancer.” He urged more research. However, the Senate committee, in its enthusiasm to solve as many of the nations’ health problems as possible, overlooked those reservations, and implied in its report that a low-fat diet could help reduce cancer risk. Cancer thus became the second “killer disease” that the Senate pinned on the back of fat consumption.

The Diet Myth: Why America's Obsessions With Weight Is Hazardous to Your Health by Paul Campos

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The confusion by Neal Stephenson

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And so only since war broke out has any progress been made here.” “No. I meant, do you know why they remodelled?” “From the looks of it I should say it was de Maintenon.” “De Maintenon?!” De Gex’s reaction told Eliza that her answer had been emphatically wrong. “Yes,” she said, “she came along in 1685, did she not? Which is when this remodel got under way…and the subject matter of the painting is so markedly Maintenon-esque.” “Correlation is not causation,” de Gex said. “They had to remodel, because of a disastrous Incident that took place in that year.” And then De Gex seemed to remember that they were in a hurry, and once again began striding toward the library. Eliza stomped along beside, and a little behind him. “You do know what happened here—?” he continued, and glanced back at her. “Something grievously embarrassing—so embarrassing that no one will tell me what it was.”

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Rationality: From AI to Zombies by Eliezer Yudkowsky

When we have a hidden motive to reject the current best option, we have a hidden motive to suspend judgment pending additional evidence, to generate more options—to find something, anything, to do instead of coming to a conclusion. A major historical scandal in statistics was R. A. Fisher, an eminent founder of the field, insisting that no causal link had been established between smoking and lung cancer. “Correlation is not causation,” he testified to Congress. Perhaps smokers had a gene which both predisposed them to smoke and predisposed them to lung cancer. Or maybe Fisher’s being employed as a consultant for tobacco firms gave him a hidden motive to decide that the evidence already gathered was insufficient to come to a conclusion, and it was better to keep looking. Fisher was also a smoker himself, and died of colon cancer in 1962.

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The Man Who Knew: The Life and Times of Alan Greenspan by Sebastian Mallaby

The implication for the fight against inflation was clear: the Fed should on no account “dissipate the gains that have been made because I suspect they are quite formidable.”12 Driving home his message to the senators, Greenspan concluded that “not only is it important to bring the inflation rate down from 10 percent to 5 percent, which everyone agrees to, but it’s increasingly becoming evident that the lower we get under 5 percent, the more stable and growing the economy.” The truth was that Greenspan was gambling with his credibility. As it turned out, the productivity justification for squeezing inflation proved even less robust than the promise of reliably lower long-term interest rates. Although Rudebusch’s data experiments found that productivity gains and stable prices might be correlated, it was impossible to establish causation; and when the Fed staff redid the calculations with revised GDP data, even the correlation crumbled.13 Barry P. Bosworth, an economist at the Brookings Institution, observed to the New York Times that earlier academic research had failed to find an inflation-productivity connection, at least in economies with inflation below 20 percent; and he suggested, rather obviously, that Greenspan might simply be looking to head off political attacks on the Fed as it raised interest rates.