{"ID":2837667,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.19284","arxiv_id":"2511.19284","title":"The Unified Non-Convex Framework for Robust Causal Inference: Overcoming the Gaussian Barrier and Optimization Fragility","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.","short_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-orde...","url_abs":"https://arxiv.org/abs/2511.19284","url_pdf":"https://arxiv.org/pdf/2511.19284v1","authors":"[\"Eichi Uehara\"]","published":"2025-11-24T16:32:07Z","proceeding":"stat.ML","tasks":"[\"stat.ML\",\"cs.LG\",\"stat.ME\"]","methods":"[]","has_code":false}