Semiparametric Estimation for Causal Mediation Analysis with Multiple Causally Ordered Mediators
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多个因果有序中介变量下的因果中介分析半参数估计
- 研究问题
- 当处理通过多个具有因果顺序、可能多维的中介影响结果时，哪些路径特定效应可以识别，又如何进行有效估计？
- 方法与思路
- 作者聚焦 Pearl 非参数结构方程模型下可识别的一组效应，通过广义中介泛函构造回归插补、加权和混合估计量。
- 材料陈述的贡献
- 摘要称，作者提出两个多重稳健且局部半参数有效的估计量，给出辅助函数达到效率所需的速率条件，并展示模拟与应用。
- 阅读关注点
- 应核对具体可识别路径及其假设、多重稳健允许的辅助模型组合、效率所需速率，并查明潜在结果计数表达的原文排版。
- 阅读边界
- 仅据所给 Crossref 摘要；并非所有路径效应都可识别，具体假设与速率未展开，潜在结果计数表达需核对原文。
原文摘要
Abstract 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.