Semiparametric Theory for Causal Mediation Analysis: efficiency bounds, multiple robustness, and sensitivity analysis.
Semiparametric Theory for Causal Mediation Analysis: efficiency bounds, multiple robustness, and sensitivity analysis.
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DOI：
10.1214/12-aos990
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发表时间：
2012-06
影响因子：
4.5
通讯作者：
Shpitser I
中科院分区：
文献类型：
--
作者：
Tchetgen EJ;Shpitser I
虽然估计点暴露对结果的边际（总）因果效应可以说是健康和社会科学实验和观察研究中最常见的目标，但近年来，研究人员对中介分析也越来越感兴趣。具体而言，在评估暴露的总效应时，研究者通常希望推断暴露效应的直接或间接途径，而不是通过或通过暴露后和结果前发生的中介变量。虽然强大的半参数方法已被开发来分析观察性研究，产生双重稳健和高效的估计边际总因果效应，类似的方法，目前缺乏中介分析。因此，本文开发了一个一般的半参数框架，用于获得关于所谓的边际自然直接和间接因果效应的推论，同时适当地考虑了大量的曝光前混杂因素的曝光和中介变量。我们的分析框架是特别有吸引力的，因为它提供了新的见解，在调解分析的背景下的效率和鲁棒性的问题。特别是，我们提出了新的多重鲁棒局部有效估计的边际自然间接和直接因果效应，并开发了一种新的双稳健敏感性分析框架的假设的中介变量的可验证性。
Whilst estimation of the marginal (total) causal effect of a point exposure on an outcome is arguably the most common objective of experimental and observational studies in the health and social sciences, in recent years, investigators have also become increasingly interested in mediation analysis. Specifically, upon evaluating the total effect of the exposure, investigators routinely wish to make inferences about the direct or indirect pathways of the effect of the exposure not through or through a mediator variable that occurs subsequently to the exposure and prior to the outcome. Although powerful semiparametric methodologies have been developed to analyze observational studies, that produce double robust and highly efficient estimates of the marginal total causal effect, similar methods for mediation analysis are currently lacking. Thus, this paper develops a general semiparametric framework for obtaining inferences about so-called marginal natural direct and indirect causal effects, while appropriately accounting for a large number of pre-exposure confounding factors for the exposure and the mediator variables. Our analytic framework is particularly appealing, because it gives new insights on issues of efficiency and robustness in the context of mediation analysis. In particular, we propose new multiply robust locally efficient estimators of the marginal natural indirect and direct causal effects, and develop a novel double robust sensitivity analysis framework for the assumption of ignorability of the mediator variable.