The credibility revolution advances the use of research designs that permit identification and estimation of causal effects. However, understanding which mechanisms produce 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 the existence of such heterogeneous treatment effects can support inferences about the activation of a mechanism. We show first that this design cannot provide evidence of mechanism activation without additional, generally implicit, assumptions. Further, even when these assumptions are satisfied, if a measured outcome is produced by a non-linear transformation of a directly-affected outcome of theoretical interest, heterogeneous treatment effects are not informative of mechanism activation. We provide novel guidance for interpretation and research design in light of these findings.

翻译：可信性革命推进了能够识别和估计因果效应的研究设计应用。然而，理解哪些机制产生了测得的因果效应仍是一项挑战。当前量化评估机制的主流方法依赖于检测处理前协变量上的异质性处理效应。本文构建了一个分析框架，用以阐明此类异质性处理效应的存在在何种条件下能够支持有关机制激活的推断。我们首先证明：若无额外的、通常隐含的假设，该设计无法为机制激活提供证据。进一步地，即使这些假设得到满足，若测得的结局变量是由理论关注的直接受影响结局变量经过非线性变换生成，则异质性处理效应对机制激活不具备信息量。基于这些发现，我们为解释与研究设计提供了新的指导。