Quadruply robust methods for causal mediation analysis

Zhen Qi; Yuqian Zhang

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arXiv · 2601.22592

Quadruply robust methods for causal mediation analysis

Zhen Qi·Yuqian Zhang

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Abstract

Estimating natural effects is a core task in causal mediation analysis. Existing triply robust (TR) frameworks (Tchetgen Tchetgen & Shpitser 2012) and their extensions have been developed to estimate the natural effects. In this work, we introduce a new quadruply robust (QR) framework that enlarges the model class for unbiased identification. We study two modeling strategies. The first is a nonparametric modeling approach, under which we propose a general QR estimator that supports the use of machine learning methods for nuisance estimation. We also study high-dimensional settings, where the dimensions of covariates and mediators may both be large. In these settings, we adopt a parametric modeling strategy and develop a model quadruply robust (MQR) estimator to limit the impact of model misspecification. Simulation studies and a real data application demonstrate the finite-sample performance of the proposed methods.

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Zhen Qi, Yuqian Zhang. 2026-01-30. Quadruply robust methods for causal mediation analysis. https://arxiv.org/abs/2601.22592

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