The Unified Non-Convex Framework for Robust Causal Inference: Overcoming the Gaussian Barrier and Optimization Fragility

2025/11/24 by Uehara, Eichi
#62G35 #62J07 #FOS: Computer and information sciences #G.3 #I.2.6 #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Methodology (stat.ME)

paper · doi:10.48550/arxiv.2511.19284

Abstract

This document proposes a Unified Robust Framework that re-engineers the estimation of the Average Treatment Effect on the Overlap (ATO). It synthesizes gamma-Divergence for outlier robustness, Graduated Non-Convexity (GNC) for global optimization, and a "Gatekeeper" mechanism to address the impossibility of higher-order orthogonality in Gaussian regimes.

Citations

• Orthogonal Machine Learning: Power and Limitations
• Balancing Covariates via Propensity Score Weighting

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