In causal analysis, understanding the causal mechanisms through which an intervention or treatment affects an outcome is often of central interest. We propose a test to evaluate (i) whether the causal effect of a treatment that is randomly assigned conditional on covariates is fully mediated by, or operates exclusively through, observed intermediate outcomes (referred to as mediators or surrogate outcomes), and (ii) whether the various causal mechanisms operating through different mediators are identifiable conditional on covariates. We demonstrate that if both full mediation and identification of causal mechanisms hold, then the conditionally random treatment is conditionally independent of the outcome given the mediators and covariates. Furthermore, we extend our framework to settings with non-randomly assigned treatments. We show that, in this case, full mediation remains testable, while identification of causal mechanisms is no longer guaranteed. We propose a double machine learning framework for implementing the test that can incorporate high-dimensional covariates and is root-n consistent and asymptotically normal under specific regularity conditions. We also present a simulation study demonstrating good finite-sample performance of our method, along with two empirical applications revisiting randomized experiments on maternal mental health and social norms.

翻译：在因果分析中，理解干预或处理影响结果的内在因果机制通常具有核心意义。本文提出一种检验方法，用于评估（i）在给定协变量条件下随机分配的处理，其因果效应是否完全由观测到的中间结果（称为中介变量或替代结果）所中介，即是否完全通过这些中间变量发挥作用；以及（ii）通过不同中介变量运作的各种因果机制在给定协变量条件下是否可识别。我们证明，若完全中介与因果机制可识别性同时成立，则在给定中介变量与协变量的条件下，该条件随机化处理与结果条件独立。此外，我们将该框架扩展至处理非随机分配的情形。研究表明，在此情况下，完全中介作用仍可检验，但因果机制的可识别性不再得到保证。我们提出一个用于实施该检验的双重机器学习框架，该框架能够纳入高维协变量，并在特定正则性条件下具有根号n相合性与渐近正态性。我们还通过模拟研究展示了该方法在有限样本下具有良好的性能，并提供了两个实证应用，重新审视了关于孕产妇心理健康与社会规范的随机实验。