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

Published: November 24, 2025 | arXiv ID: 2511.19284v1

By: Eichi Uehara

Potential Business Impact:

Improves how we measure treatment effects fairly.

Business Areas:
A/B Testing Data and Analytics

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.

Page Count
11 pages

Category
Statistics:
Machine Learning (Stat)