Causal inference requires evaluating models on balanced distributions between treatment and control groups, while training data often exhibits imbalance due to historical decision-making policies. Most conventional statistical methods address this distribution shift through inverse probability weighting (IPW), which requires estimating propensity scores as an intermediate step. These methods face two key challenges: inaccurate propensity estimation and instability from extreme weights. We decompose the generalization error to isolate these issues--propensity ambiguity and statistical instability--and address them through an adversarial loss function. Our approach combines distributionally robust optimization for handling propensity uncertainty with weight regularization based on weighted Rademacher complexity. Experiments on synthetic and real-world datasets demonstrate consistent improvements over existing methods.

翻译：因果推断需要在处理组与对照组之间平衡的分布上评估模型，而训练数据常因历史决策策略而呈现不平衡。大多数传统统计方法通过逆概率加权（IPW）处理这种分布偏移，这需要估计倾向得分作为中间步骤。这些方法面临两个关键挑战：倾向得分估计不准确以及极端权重导致的不稳定性。我们通过分解泛化误差来分离这些问题——倾向模糊性与统计不稳定性——并通过对抗性损失函数加以解决。我们的方法结合了处理倾向不确定性的分布鲁棒优化与基于加权Rademacher复杂度的权重正则化。在合成和真实数据集上的实验表明，该方法相较于现有方法取得了持续改进。