Kieran Healy and Laurie Paul wrote a new article, “Transformative Treatments,” (see also here) which reminds me a bit of my article with Guido, “Why ask why? Forward causal inference and reverse causal questions.” Healy and Paul’s article begins:
Contemporary social-scientific research seeks to identify specific causal mechanisms for outcomes of theoretical interest. Experiments that randomize populations to treatment and control conditions are the “gold standard” for causal inference. We identify, describe, and analyze the problem posed by transformative treatments. Such treatments radically change treated individuals in a way that creates a mismatch in populations, but this mismatch is not empirically detectable at the level of counterfactual dependence. In such cases, the identification of causal pathways is underdetermined in a previously unrecognized way. Moreover, if the treatment is indeed transformative it breaks the inferential structure of the experimental design. . . .
I’m not sure exactly where my paper with Guido fits in here, except that the idea of the “treatment” is so central to much of causal inference, that sometimes researchers seem to act as if randomization (or, more generally, “identification”) automatically gives validity to a study, as if randomization plus statistical significance equals scientific discovery. The notion of a transformative treatment is interesting because it points to a fundamental contradiction in how we typically think of causality, in that on one hand “the treatment” is supposed to be transformative and have some clearly-defined “effect,” while on the other hand the “treatment” and “control” are typically considered symmetrically in statistical models. I pick at this a bit in this 2004 article on general models for varying treatment effects.
P.S. Hey, I just remembered—I discussed this a couple of other times on this blog: