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 an additional, generally implicit, assumption. Further, even when this assumption is 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：可信性革命推动了能够识别和估计因果效应的研究设计的应用。然而，理解哪些机制产生了所测量的因果效应仍然是一个挑战。当前量化评估机制的主流方法依赖于检测与预处理协变量相关的异质性处理效应。本文构建了一个框架，用以理解此类异质性处理效应的存在何时能够支持关于机制激活的推断。我们首先证明，在没有额外（通常隐含的）假设的情况下，该设计无法提供机制激活的证据。此外，即使该假设得到满足，若测量结果是由理论关注直接受影响结果经过非线性变换产生，则异质性处理效应对于机制激活并不具有信息性。基于这些发现，我们为解释和研究设计提供了新的指导。