The New Science of Cause and Effect
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 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.
A causal link between smoking and cancer was not acknowledged by the US Surgeon General until 1964.
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Blink 3 von 12 - Eine kurze Geschichte der Menschheit
von Yuval Noah Harari
