The human mind has incredible predictive abilities, but our explanatory abilities do not always turn out to be as equally incredible. Prediction is relatively easy when compared to explanation. Animals can predict where a food source will be without being able to explain how it got there. For most of human history our ancestors were able to predict that the sun would rise the next day without having any way of explaining why it would rise. Computer programs today can predict our next move in chess but few can explain their prediction or why we would make the choice that was predicted.
As Judea Pearl writes in The Book of Why, “Good predictions need not have good explanations. The owl can be a good hunter without understanding why the rat always goes from point A to point B.” Prediction is possible with statistics and good observations. With a large enough database, we can make a prediction about what percentage of individuals will have negative reactions to medications, we can predict when a traffic jam will occur, and we can predict how an animal will behave. What is harder, according to Pearl, is moving to the stage where we describe why we observe the relationships that statistics reveal.
Statistics alone cannot tell us why particular patterns emerge. Statistics cannot identify causal structures. As a result, we continually tell ourselves that correlation is not causation and that we can only determine what relationships are truly causal through randomized controlled trials. Pearl would argue that this is incorrect, and he would argue that this idea results from the fact that statistics is trying to answer a completely different question than causation. Approaching statistical questions from a causal lens may lead to inaccurate interpretations of data or “p-hacking” an academic term used to describe efforts to get the statistical results you wanted to see. The key is not hunting for causation within statistics, but understanding causation and supporting it through evidence uncovered via statistics.
Seeing the difference between causation and statistics is helpful when thinking about the world. Being stuck without a way to see and determine causation leads to situations like tobacco companies claiming that cigarettes don’t cause cancer or oil and gas companies claiming that humans don’t contribute to global warming. Causal thinking, however, utilizes our ability to develop explanations and applies those explanations to the world. Our ability to predict different outcomes based on different interventions helps us interpret and understand the data that the world produces. We may not see the exact picture in the data, but we can understand it and use it to help us make better decisions that will lead to more accurate causal understandings over time.