论文标题
与多个调解人的介入效应的非参数推断
Nonparametric inference for interventional effects with multiple mediators
论文作者
论文摘要
了解干预对结果产生影响的途径是一个共同的科学目标。丰富的文献提供了各种全面干预效果的各种分解，以特定途径。介入直接和间接效应提供了一种这种分解。这些效果的现有估计器基于具有置信区间估计的参数模型，该模型通过非参数bootstrap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，以促进封闭形式的置信区间和假设检验的构建。最后，我们证明了所提出的估计量的多种鲁棒性特性。模拟表明，基于大样本理论的推论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算介入的调解效应的方法。
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. Finally, we demonstrate 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.
