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*The Book of Why *(2018) introduces basic concepts of statistical methods of argumentation and makes the case for a mathematical model of causation. For decades, the mantra “correlation does not imply causation” has been hammered home by statisticians. The result has been stagnation in many forms of research, and this book aims to push back against this trend.

- Followers of national news and government reports
- Social scientists reliant on big data
- Humanities students looking to break with dominant paradigms

Judea Pearl is a computer scientist and philosopher. In 2011, he won the Turing Award, the most prestigious prize in computer science. He is the author of* Causality*, *Probabilistic* *Reasoning in Intelligent Systems *and *Causal Inference in Statistics*.

Dana Mackenzie is a writer and mathematician. He is the author of *The Universe in Zero Words* and *The Big Splat, or How Our Moon Came to Be*.

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*The Book of Why *(2018) introduces basic concepts of statistical methods of argumentation and makes the case for a mathematical model of causation. For decades, the mantra “correlation does not imply causation” has been hammered home by statisticians. The result has been stagnation in many forms of research, and this book aims to push back against this trend.

If you’ve spent any time near an institute of higher learning or, frankly, if you’ve ever heard a brainiac dismissing government reports on the news, you’ll likely have heard the phrase “correlation does not imply causation” repeated ad nauseam. It has virtually been accepted as fact for the last few decades.

In part, this is down to the fact that *causation* has been downplayed as an idea by the scientific community. At the start of the twentieth century, English mathematician Karl Pearson epitomized this view.

Pearson’s biometrics lab was the world’s leading authority in statistics, and he liked to claim that science was nothing more than pure data. The idea was that because causation could not be proven, it could not be represented as data. Therefore, he saw causation as scientifically invalid.

Pearson liked to prove his point by singling out correlations that he considered spurious. A favorite was the observation that if a nation consumes more chocolate per capita, it produces more Nobel Prize winners. To him, it was a meaningless correlation, so looking for causation was unnecessary.

But this attempt at ridicule actually hides a causative factor; it *is* likelier that *wealthier* nations consume more chocolate, just as it’s likelier that they’ll produce scientific advances noticeable to the Nobel committee!

On top of that, it later turned out that causation could be represented mathematically. This is what geneticist Sewall Wright showed while researching at Harvard University in 1912.

Wright was studying the markings on guinea pigs’ coats to determine the extent to which they were hereditary. He found the answer to this causal question by using data.

It began with a mathematical diagram. Wright drew arrows connecting causes and outcomes, linking the colors of the animals’ coats to contributing factors in their immediate environment and development.

Wright also developed a *path diagram *to represent these relationships, in which a “greater-than” sign (>) signifies “has an effect on.” For instance: *developmental factors > gestation period > coat pattern.*

Wright then turned this diagram into an algebraic equation, using the collected data. It demonstrated that 42 percent of a given coat pattern was caused by heredity, while 58 percent was the result of developmental factors.

Given the scientific climate, Wright came in for some stick: he was so vehemently attacked that his methods for establishing causation from correlation were buried for decades.

But times have changed; it is now finally time to revive his work. Research fields from medicine to climate science are now beginning to welcome causation as a principle. Surely the Causal Revolution has begun.

- The notion of causation has been disparaged by some statisticians.
- Data alone can mislead when causality is neglected.
- The first rung of the Ladder of Causation is concerned with association and probability.
- The second rung of the ladder is intervention, which we use both day to day and in research.
- The third and final rung of the ladder involves getting to grips with counterfactuals.
- Controlling for confounders is important in establishing causality.
- The identification of a mediator can be vital in establishing correct causality.
- Factors and their relationships can be expressed with mathematical formulae, which could be turned into algorithms.
- Final summary