Heterogeneous Treatment Effects and Causal Mechanisms

The credibility revolution has promoted the adoption of research designs that permit identification and estimation of causal effects. Understanding which mechanisms drive measured causal effects remains a challenge. A dominant current approach to the quantitative evaluation of mechanisms relies on the detection of heterogeneous treatment effects with respect to pre-treatment covariates. This paper develops a framework to understand when such heterogeneous treatment effects can support substantive inferences about the activation of a mechanism. We show first that this design does not provide evidence of mechanism activation without additional assumptions. Further, even when these assumptions are satisfied, if a measured outcome is produced by a non-linear transformation of a latent variable of theoretical interest, heterogeneous treatment effects are not necessarily informative of mechanisms. We provide new guidance for interpretation and research design in light of these findings.