🤖 AI Summary
Existing methods struggle to effectively identify and infer causal mediation pathways involving multiple time-varying mediators. This work proposes the first general analytical framework capable of decomposing total effects into path-specific effects, accommodating both continuous and categorical outcomes. The approach innovatively constructs a mediation path testing strategy that rigorously controls Type I error under composite null hypotheses by integrating studentized statistics with data splitting techniques, thereby enabling identifiability and efficient inference in complex mediation chains. Extensive simulations and two large-scale empirical studies demonstrate the method’s marked advantages in estimation accuracy, inferential validity, and statistical power.
📝 Abstract
Mediation analysis plays an essential role in uncovering the mechanisms by which an exposure influences an outcome through intermediate pathways. While methodological advances for single-mediator settings are well established, rigorous tools for handling multiple, sequentially ordered mediators remain underdeveloped. Such settings are common in applications like longitudinal cohort studies, where exposures operate through complex chains of mediators over time. In this paper, we establish a general framework for sequentially ordered mediators that enables the identification and formal decomposition of the total effect into component path-specific effects. We also develop estimation procedures for mediation estimands with both continuous and categorical outcomes. Furthermore, we introduce a new testing strategy to conduct inference using a studentized statistic combined with data-splitting. This approach achieves valid Type I error control under the composite null across diverse data-generating mechanisms. Through extensive simulations and applications to two large-scale empirical studies, we demonstrate that the proposed methodology provides reliable estimation, valid inference, and improved power for discovering novel mediation pathways.