Why ask Why? Forward Causal Inference and Reverse Causal Questions
The statistical and econometrics literature on causality is more focused on "effects of causes" than on "causes of effects." That is, in the standard approach it is natural to study the effect of a treatment, but it is not in general possible to define the causes of any particular outcome. This has led some researchers to dismiss the search for causes as "cocktail party chatter" that is outside the realm of science. We argue here that the search for causes can be understood within traditional statistical frameworks as a part of model checking and hypothesis generation. We argue that it can make sense to ask questions about the causes of effects, but the answers to these questions will be in terms of effects of causes.
We thank Judea Pearl and several other blog commenters for helpful input and the National Science Foundation for partial support of this work. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.