Department of Industrial Engineering & Decision Analytics - Identification and multiply robust estimation in causal mediation analysis across principal strata
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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.
Fan Li is an Associate Professor in the Department of Biostatistics at Yale School of Public Health and holds a secondary appointment in the Section of Cardiovascular Medicine at Yale School of Medicine. He received his PhD in Biostatistics from Duke University in 2019 and joined the Yale faculty in the same year. Dr. Li's research interests primarily lie in causal inference methodology for complex randomized experiments and observational studies, including parametric and nonparametric machine learning methods for studying causal mediation, principal stratification, and treatment effect heterogeneity. He is the principal investigator of multiple grants and awards from the United States National Institutes of Health and the Patient-Centered Outcomes Research Institute (PCORI). Dr. Li's scholarship has been recognized with prestigious honors, including the 2026 COPSS Emerging Leader Award and the 2026 Early Career Research Excellence Award from the Association of Schools and Programs of Public Health.