The Unified Non-Convex Framework for Robust Causal Inference: Overcoming the Gaussian Barrier and Optimization Fragility
Machine Learning
2025-11-25 v1 Machine Learning
Methodology
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.
Keywords
Cite
@article{arxiv.2511.19284,
title = {The Unified Non-Convex Framework for Robust Causal Inference: Overcoming the Gaussian Barrier and Optimization Fragility},
author = {Eichi Uehara},
journal= {arXiv preprint arXiv:2511.19284},
year = {2025}
}
Comments
10 pages, 1 table