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

Eichi Uehara
11/24/2025
stat.MLcs.LGstat.ME

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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.

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Cite this paper

@article{uehara2025unified,
  title  = {The Unified Non-Convex Framework for Robust Causal Inference: Overcoming the Gaussian Barrier and Optimization Fragility},
  author = {Eichi Uehara},
  year   = {2025},
  eprint = {2511.19284},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2511.19284v1}
}

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