Teaching Causal Inference

In the JSM 2013 I participated in a round table about teaching causal inference, where I had the privilege of meeting Judea Pearl. The reason I was so interested in the subject, besides the fact that causal inference is of great interest to me, was that if I had to name the biggest gap I had in my statistical training, it would be causal inference for sure.

They told me that regression was a mathematical equation and the coefficient of X mean "If you increase X by one unit this coefficient is how much Y will increase in average". Right there, in the very explanation of the coefficient was the idea of causality, yet causality was never talked about.

It turns out that it is not only that. In the real world, everything is about causality. People may not say it or they may not even know it (!) but what they want when they are running a regression model is a measure of causal effect. Why then in a top statistical course we don't hear anything about the causal assumptions we are making? Or  confounder? It seems to me that not only causal inference should be talked more about, it should perhaps be the core of a statistical training.

In the experimental design part of the training we are taken through the causal inference in a way, but that never gets linked to the regression analysis, or SEM, on observational data, which is what we have most of the time. Statisticians learn that everything they do is conditional on the validity of assumptions, yet they dont seem to want to think or to talk about or to make assumptions about causality.

So, that round table was very interesting for what I learnt, for what we discussed and for the folks I met. I have no experience on teaching, so I don't know how to do this, but I am contemplating diving a little deeper into this, by perhaps searching the related literature and putting together some material that make it easier to understand causality and how it relates to statistical models. I could also use for this many of the ideas from our round table. And perhaps it could be a joint effort. I think such a thing, if mainstream, would be quite helpful not only for the current students but also maybe for the entire sciences in how it would improve the quality of the research that are currently done.