Joint Adaptive Penalty for Unbalanced Mediation Pathways

Mediation analysis has been widely used to investigate how a treatment influences an outcome through intermediate variables, known as mediators. Analyzing a mediation mechanism typically requires assessing multiple model parameters that characterize distinct pathwise effects. Classical methods that estimate these parameters individually can be inefficient, particularly when the underlying pathwise effects exhibit substantial imbalance. To address this challenge, this work proposes a new joint adaptive penalty that integrates information across entire mediation mechanisms, thereby enhancing both parameter estimation and pathway selection. We establish theoretical guarantees for the proposed method under an asymptotic framework and conduct extensive numerical studies to demonstrate its superior performance in scenarios with unbalanced mediation pathways.