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arXiv · 2607.02901

Proximal Mediation Analysis with Unmeasured Treatment-Induced Confounding

Xiaoying Zhang·Jiawei Shan·Wei Li
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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.

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Xiaoying Zhang, Jiawei Shan, Wei Li. 2026-07-03. Proximal Mediation Analysis with Unmeasured Treatment-Induced Confounding. https://arxiv.org/abs/2607.02901

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