Talking About Causation

In The Book of Why Judea Pearl argues that humans are better at modeling, predicting, and identifying causation than we like to acknowledge. For Pearl, the idea that we can see direct causation and study it scientifically is not a radical and naïve belief, but a common sense and defensible observation about human pattern recognition and intuition of causal structures in the world. He argues that we are overly reliant on statistical methods and randomized controlled trials that suggest relationships, but never tell us exactly what causal mechanisms are at the heart of such relationships.
One of the greatest frustrations for Pearl is the limitations he feels have been placed around ideas and concepts for causality. For Pearl, there is a sense that certain research, certain ways of talking about causality, and certain approaches to solving problems are taboo, and that he and other causality pioneers are unable to talk in a way that might lead to new scientific breakthroughs. Regarding a theory of causation and a the history of our study of causality, he writes, “they declared those questions off limits and turned to developing a thriving causality-free enterprise called statistics.”
Statistics doesn’t tell us a lot about causality. Statistical thinking is a difficult way for most people to think, and for non-statistically trained individuals it leads to frustrations. I remember around the time of the 2020 election that Nate Silver, a statistics wonk at, posted a cartoon where one person was trying to explain the statistical chance of an outcome to another person. The other person interpreted statistical chances as either 50-50 or all or nothing. They interpreted a low probability event as a certainty that something would not happen and interpreted a high probability event as a certainty that it would happen, while more middle ground probabilities were simply lumped in as 50-50 chances. Statistics helps us understand these probabilities in terms of the outcomes we see, but doesn’t actually tell us anything about the why behind the statistical probabilities. That, I think Pearl would argue, is part of where the confusion for the individual in the cartoon who had trouble with statistics stems from.
Humans think causally, not statistically. However, our statistical studies and the accepted way of doing science pushes against our natural causal mindsets. This has helped us better understand the world in many ways, but Pearl argues that we have lost something along the way. He argues that we needed to be building better ways of thinking about causality and building models and theories of causality at the same time that we were building and improving our studies of statistics. Instead, statistics took over as the only responsible way to discuss relationships between events, with causality becoming taboo.
“When you prohibit speech,” Pearl writes, “you prohibit thought and stifle principles, methods, and tools.” Pearl argues that this is what is happening in terms of causal thinking relative to statistical thinking. I think he, and other academics who make similar speech prohibition arguments, are hyperbolic, but I think it is important to consider whether we are limiting speech and knowledge in an important way. In many studies, we cannot directly see the causal structure, and statistics does have ways of helping us better understand it, even if it cannot point to a causal element directly. Causal thinking alone can lead to errors in thinking, and can be hijacked by those who deliberately want to do harm by spreading lies and false information. Sometimes regressions and correlations hint at possible causal structures or completely eliminate others from consideration. The point is that statistics is still useful, but that it is something we should lean into as a tool to help us identify causality, not as the endpoint of research beyond which we cannot make any assumptions or conclusions.
Academics, such as Pearl and some genetic researchers, may want to push forward with ways of thinking that others consider taboo, and sometimes fail to adequately understand and address the concerns that individuals have about the fields. Addressing these areas requires tact and an ability to connect research in fields deemed off limits to the fields that are acceptable. Statistics and a turn away from a language of causality may have been a missed opportunity in scientific understanding, but it is important to recognize that human minds have posited impossible causal connections throughout history, and that we needed statistics to help demonstrate how impossible these causal chains were. If causality became taboo, it was at least partly because there were major epistemic problems in the field of causality. The time may have come for addressing causality more directly, but I am not convinced that Pearl is correct in arguing that there is a prohibition on speech around causality, at least not if the opportunity exists to tactfully and responsibly address causality as I think he does in his book.

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