Causal Versus Statistical Thinking

Humans are naturally causal thinkers. We observe things happening in the world and begin to apply a causal reason to them, asking what could have led to the observation we made. We attribute intention and desire to people and things, and work out a narrative that explains why things happened the way they did.


The problem, however, is that we are prone to lots of mistakes when we think in this way. Especially when we start looking at situations that require statistical thinking. In his book Thinking Fast and Slow, Daniel Kahneman writes the following:


“The prominence of causal intuitions is a recurrent theme in this book because people are prone to apply causal thinking inappropriately, to situations that require statistical reasoning. Statistical thinking derives conclusions about individual cases from properties of categories and ensembles. Unfortunately, System 1 does not have the capability for this mode of reasoning; system 2 can learn to think statistically, but few people receive the necessary training.”


System 1 is our fast brain. It works quickly to identify associations and patters, but it doesn’t take in a comprehensive set of information and isn’t able to do much serious number crunching. System 2 is our slow brain, able to do the tough calculations, but limited to work on the set of data that System 1 is able to accumulate. Also, System 2 is only active for short periods of time, and only when we consciously make use of it.


This leads to our struggles with causal thinking. We have to take in a wide range of possibilities, categories, and ranges of combinations. We have to make predictions and understand that in some set of instances we will see one outcome, but in another set of circumstances we may see a different outcome. Statistical thinking doesn’t pin down a concrete answer the way our causal thinking likes. As a result, we reach conclusions based on incomplete considerations, we ignore some important pieces of information, and we assume that we are correct because our answer feels correct and satisfies some criteria. Thinking causally can be powerful and useful, but only if we fully understand the statistical dimensions at hand, and can fully think through the implications of the causal structures we are defining.

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