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，理解干预或治疗影响结果的因果机制通常是核心关注点。我们提出一种检验方法，用于评估：(i) 在协变量条件下随机分配的治疗的因果效应是否完全通过观测到的中间结果（称为中介变量或替代终点）传导，或仅通过这些变量发挥作用；(ii) 通过不同中介变量运作的各种因果机制在协变量条件下是否可识别。我们证明，若完全中介和因果机制可识别性同时成立，则条件随机治疗在给定中介变量和协变量后与结果条件独立。此外，我们将框架扩展至非随机分配治疗的场景，表明此时完全中介仍可检验，但因果机制的可识别性不再有保证。我们提出一种双重机器学习框架用于实施该检验，该框架可纳入高维协变量，并在特定正则条件下具有根号n一致性和渐近正态性。我们还通过仿真研究展示该方法良好的有限样本性能，并基于两项关于孕产妇心理健康和社会规范的随机实验进行实证复现分析。