Proximal Mediation Analysis with Unmeasured Treatment-Induced Confounding

2026-07-02

AI Summary

This study addresses the non-identifiability of natural indirect effects in mediation analysis due to unobserved treatment-induced confounding. To overcome this challenge, the authors introduce an observable proxy variable for the unmeasured confounder and establish sufficient conditions for identifying causal mediation effects. Four proxy-based identification strategies are proposed, accompanied by a multiply robust and semiparametric locally efficient estimator that incorporates flexible machine learning techniques to model nuisance parameters. The validity and practical utility of the proposed approach are demonstrated through comprehensive simulation studies and an empirical application examining the mediating mechanisms through which racial discrimination affects life satisfaction.

Abstract

Mediation analysis provides a central framework for elucidating causal mechanisms, yet its application is often impeded by treatment-induced confounding, under which the widely used natural mediation effects are generally unidentifiable. Interventional effects have been proposed as an alternative when these confounders are observable; however, identifying and estimating interventional effects remains challenging when confounders are unmeasured. In this paper, we address this issue by using observed variables as proxies for unmeasured treatment-induced confounders. We establish four proximal identification results and develop a multiply robust, semiparametric locally efficient estimator that accommodates flexible machine learning methods for nuisance parameter estimation. The proposed approach is illustrated through simulation studies and a real-data application evaluating racial disparities in life satisfaction mediated by discrimination.

Problem

• mediation analysis
• treatment-induced confounding
• unmeasured confounders
• causal inference
• proxy variables

Innovation

• proximal mediation analysis
• treatment-induced confounding
• unmeasured confounders
• multiply robust estimation
• interventional effects