论文标题
通过多个因果下降的介体进行因果中介分析的半参数估计
Semiparametric Estimation for Causal Mediation Analysis with Multiple Causally Ordered Mediators
论文作者
论文摘要
因果中介分析涉及治疗影响结果的途径。尽管大多数调解文献都专注于单个调解人的设置，但繁荣的研究线已经检查了涉及多个介体的设置，在该设置下，通常感兴趣的是路径特异性效应（PSES）。当治疗效果通过K（\ GEQ1）因果序（可能是多元介体）运行时，我们考虑了PSE的估计。在这种情况下，许多因果路径的PSE并未被非参数鉴定，我们专注于一组在Pearl的非参数结构方程模型下鉴定的PSE。这些PSE被定义为2^{K+1}潜在结果之间的对比，并通过我们称为广义的中介功能（GMF）确定。我们引入了一系列回归输入，加权和“混合”估计器，尤其是GMF的两个K+2-bobust和局部半参数有效估计器。后一个估计量非常适合使用数据自适应方法来估计其滋扰功能。我们建立了半摩托效率的滋扰功能所需的速率条件。我们还讨论了我们的框架如何适用于在经验应用中可能特别感兴趣的几个估计值。提出的估计量通过模拟研究和经验例子进行了说明。
Causal mediation analysis concerns the pathways through which a treatment affects an outcome. While most of the mediation literature focuses on settings with a single mediator, a flourishing line of research has examined settings involving multiple mediators, under which path-specific effects (PSEs) are often of interest. We consider estimation of PSEs when the treatment effect operates through K(\geq1) causally ordered, possibly multivariate mediators. In this setting, the PSEs for many causal paths are not nonparametrically identified, and we focus on a set of PSEs that are identified under Pearl's nonparametric structural equation model. These PSEs are defined as contrasts between the expectations of 2^{K+1} potential outcomes and identified via what we call the generalized mediation functional (GMF). We introduce an array of regression-imputation, weighting, and "hybrid" estimators, and, in particular, two K+2-robust and locally semiparametric efficient estimators for the GMF. The latter estimators are well suited to the use of data-adaptive methods for estimating their nuisance functions. We establish the rate conditions required of the nuisance functions for semiparametric efficiency. We also discuss how our framework applies to several estimands that may be of particular interest in empirical applications. The proposed estimators are illustrated with a simulation study and an empirical example.
