Andrew Gelman & Guido Imbens
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 determine 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.
I haven’t had a chance to digest this yet, but it’s an interesting topic. It’s particularly relevant to system dynamics modeling, where we are seldom seeking only y = f(x), but rather an endogenous theory where x = g(y) also.
See also: Causality in Nonlinear Systems
h/t Peter Christiansen.