Joint Adaptive Penalty for Unbalanced Mediation Pathways

2025-09-29

AI Summary

Traditional mediation analysis suffers from inefficiency in estimating path effects when pathway effects are severely imbalanced, as it treats each path parameter independently. To address this, we propose a joint adaptive penalization method that embeds all mediation pathways into a unified modeling framework, leveraging inter-path correlations to jointly optimize parameter estimation and pathway selection. Our approach employs a data-driven adaptive weighting scheme that ensures asymptotic unbiasedness and variable selection consistency while substantially improving estimation accuracy and model sparsity under imbalance. We establish its oracle property theoretically. Extensive numerical simulations and empirical analyses demonstrate that the proposed method outperforms both independent estimation and existing joint approaches across key metrics—including pathway identification accuracy, parameter estimation bias, and prediction error.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Mixed Discrete/Continuous Search
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

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.

Problem

• Addresses inefficient parameter estimation in unbalanced mediation pathways
• Proposes joint adaptive penalty for integrated mediation mechanism analysis
• Enhances pathway selection accuracy under substantial effect imbalance

Innovation

• Joint adaptive penalty integrates entire mediation mechanisms
• Method enhances parameter estimation and pathway selection
• Theoretical guarantees established under asymptotic framework