We consider assessing causal mediation in the presence of a post-treatment event (examples include noncompliance, a clinical event, or a terminal event). We identify natural mediation effects for the entire study population and for each principal stratum characterized by the joint potential values of the post-treatment event. We derive efficient influence functions for each mediation estimand, which motivate 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 illustrate our methods via simulations and two real data examples.

翻译：我们考虑在存在治疗后事件（例如不依从、临床事件或终末事件）的情况下评估因果中介效应。我们识别了整个研究人群以及由治疗后事件联合潜在值特征化的每个主层的自然中介效应。我们推导了每个中介估计量的有效影响函数，这激发了一组用于推断的多重稳健估计量。这些多重稳健估计量在四种错误设定类型下保持一致，并在所有干扰模型正确设定时有效。我们通过模拟和两个真实数据示例说明了我们的方法。