Semiparametric estimation for causal mediation analysis with multiple causally ordered mediators

• 成果类型: Article
• 署名作者: Zhou, Xiang
• 署名单位: Harvard University
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/rssb.12487
• 发表日期: 2022
• 页码: 794-821
• 关键词: effect decomposition; Robust Estimation; Missing Data; inference; education; models

摘要

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(>= 1) 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 2K+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.