Matt Fox writes:
I teach various Epidemiology courses in Boston and in South Africa and have been reading your blog for the past year or so and used several of your examples in class . . . I am curious to know why you are skeptical of structural models. Much of my training has been in how essential these models are and I rarely hear the other side of the debate.
I’ve never used structural models myself. They just seem to require so many assumptions that I don’t know how to interpret them. (Of course the same could be said of Bayesian methods, but that doesn’t seem to bother me at all.) One thing I like to say is that in observational settings I feel I can interpret at most one variable causally. The difficulty is that it’s hard to control for things that happen after the variable that you’re thinking of as the “treatment.”
To put it another way, there’s a research paradigm in which you fit a model–maybe a regression, maybe a structural equations model, maybe a multilevel model, whatever–and then you read off the coefficients, with each coefficient telling you something. You gather these together and those are your conclusions.
My paradigm is a bit different. I sometimes say that each causal inference requires its own analysis and maybe its own experiment. I find it difficult to causally interpret several different coefficients from the same model.