Quadratic robust methods for causal mediation analysis

2026-01-30

AI Summary

This study addresses the susceptibility of natural effect estimation in causal mediation analysis to model misspecification by proposing a quadruply robust (QR) estimation framework. The approach integrates nonparametric machine learning with high-dimensional parametric modeling, yielding the first QR and model quadruply robust (MQR) estimators tailored for settings involving high-dimensional covariates and mediators. This advancement substantially broadens the class of models under which unbiased identification is achievable. Both theoretical analysis and empirical evaluations demonstrate that the proposed estimators exhibit strong finite-sample performance and enhanced robustness, maintaining accuracy even when some nuisance components are misspecified.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Intelligent Robots: State Estimation

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• causal mediation analysis
• natural effects
• robust estimation
• model misspecification
• high-dimensional data

Innovation

• quadruply robust
• causal mediation analysis
• machine learning
• high-dimensional data
• model misspecification