Estimating Heterogeneous Treatment Effects (HTE) in industrial applications such as AdTech and healthcare presents a dual challenge: extreme class imbalance and heavy-tailed outcome distributions. While the X-Learner framework effectively addresses imbalance through cross-imputation, we demonstrate that it is fundamentally vulnerable to "Outlier Smearing" when reliant on Mean Squared Error (MSE) minimization. In this failure mode, the bias from a few extreme observations ("whales") in the minority group is propagated to the entire majority group during the imputation step, corrupting the estimated treatment effect structure. To resolve this, we propose the Robust X-Learner (RX-Learner). This framework integrates a redescending γ-divergence objective -- structurally equivalent to the Welsch loss under Gaussian assumptions -- into the gradient boosting machinery. We further stabilize the non-convex optimization using a Proxy Hessian strategy grounded in Majorization-Minimization (MM) principles. Empirical evaluation on a semi-synthetic Criteo Uplift dataset demonstrates that the RX-Learner reduces the Precision in Estimation of Heterogeneous Effect (PEHE) metric by 98.6% compared to the standard X-Learner, effectively decoupling the stable "Core" population from the volatile "Periphery".

翻译：在广告科技与医疗健康等工业应用中估计异质性处理效应面临双重挑战：极端的类别不平衡与厚尾的结果分布。尽管X-Learner框架通过交叉插补有效应对不平衡问题，但我们证明其依赖均方误差最小化时，本质上易受“异常值污染”的影响。在此失效模式下，少数群体中少数极端观测值（“鲸鱼”）产生的偏差会在插补步骤中传播至整个多数群体，从而破坏估计的处理效应结构。为解决此问题，我们提出稳健X-Learner框架。该框架将重下降γ散度目标——在高斯假设下结构等价于Welsch损失——集成至梯度提升机制中。基于优化-最小化原理，我们进一步采用代理海森矩阵策略以稳定非凸优化过程。在半合成Criteo提升数据集上的实证评估表明，相较于标准X-Learner，RX-Learner将异质性效应估计精度指标降低了98.6%，有效实现了稳定“核心”群体与波动“边缘”群体的解耦。