Identification and multiply robust estimation in causal mediation analysis across principal strata
研究了存在治疗后事件（如不依从、临床事件或死亡）时的因果中介效应，提出了对整个研究人群及各主分层的自然中介效应的识别方法，并构建了多重稳健估计量，允许四种模型设定错误下仍保持一致估计，同时利用数据自适应机器学习实现高效推断。
Abstract We consider assessing causal mediation in the presence of a posttreatment event (examples include noncompliance, a clinical event, or death). We identify natural mediation effects for the entire study population and for each principal stratum characterized by the joint potential values of the posttreatment event. We derive the efficient influence function for each mediation estimand, which motivates a set of multiply robust estimators for inference. The multiply robust estimators are consistent under four types of misspecifications and are efficient when all nuisance models are correctly specified. We also develop a nonparametric efficient estimator that leverages data-adaptive machine learners to achieve efficient inference and discuss sensitivity methods to address key identification assumptions. We illustrate our methods via simulations and two real data examples.