While 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, through a mediator variable or not, 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.

本研究开发了一个半参数框架，以获取关于所谓的边际自然直接和间接因果效应的推论，同时适当考虑曝露和中介变量的大量先前混淆因素，尤其是在中介分析方面提出了增量稳健的本地有效估计量和一种新的双重稳健灵敏度分析框架。