Understanding the pathways whereby an intervention has an effect on an outcome is a common scientific goal. A rich body of literature provides various decompositions of the total intervention effect into pathway-specific effects. Interventional direct and indirect effects provide one such decomposition. Existing estimators of these effects are based on parametric models with confidence interval estimation facilitated via the nonparametric bootstrap. We provide theory that allows for more flexible, possibly machine learning-based, estimation techniques to be considered. In particular, we establish weak convergence results that facilitate the construction of closed-form confidence intervals and hypothesis tests and prove multiple robustness properties of the proposed estimators. Simulations show that inference based on large-sample theory has adequate small-sample performance. Our work thus provides a means of leveraging modern statistical learning techniques in estimation of interventional mediation effects.

中文翻译：

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多介质干预效果的非参数推断

了解干预对结果产生影响的途径是一个共同的科学目标。大量文献提供了将总干预效应分解为途径特异性效应的各种分解方法。干预的直接和间接影响提供了一种这样的分解。这些影响的现有估计器基于参数模型，其中置信区间估计通过非参数引导程序促进。我们提供的理论允许考虑更灵活、可能基于机器学习的估计技术。特别是，我们建立了弱收敛结果，这有助于构建封闭式置信区间和假设检验，并证明所提出的估计量的多重稳健性。仿真表明，基于大样本理论的推理具有足够的小样本性能。因此，我们的工作提供了一种利用现代统计学习技术来估计干预中介效应的方法。