提出统一框架，提升因果推断在异常值和优化难题下的稳定性。

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

• 融合伽马散度与渐进非凸优化，增强对异常值的鲁棒性。
• 引入'守门员'机制，突破高斯环境下高阶正交性失效的瓶颈。
• 适用于存在噪声或分布偏移的真实世界因果分析场景。

本文提出一种统一的稳健框架，重新设计平均处理效应在重叠区域（ATO）的估计方法。该框架融合伽马散度以增强对异常值的鲁棒性，采用渐进非凸性（GNC）实现全局优化，并引入'守门员'机制，解决高斯分布下高阶正交性无法成立的根本难题。该方法在复杂数据条件下显著提升了因果效应估计的稳定性和准确性，尤其适用于存在分布偏移或异常观测的真实世界应用场景。

原文摘要 · Abstract (English)

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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