Heterogeneous treatment effect (HTE) estimation from observational data poses significant challenges due to treatment selection bias. Existing methods address this bias by minimizing distribution discrepancies between treatment groups in latent space, focusing on global alignment. However, the fruitful aspect of local proximity, where similar units exhibit similar outcomes, is often overlooked. In this study, we propose Proximity-aware Counterfactual Regression (PCR) to exploit proximity for representation balancing within the HTE estimation context. Specifically, we introduce a local proximity preservation regularizer based on optimal transport to depict the local proximity in discrepancy calculation. Furthermore, to overcome the curse of dimensionality that renders the estimation of discrepancy ineffective, exacerbated by limited data availability for HTE estimation, we develop an informative subspace projector, which trades off minimal distance precision for improved sample complexity. Extensive experiments demonstrate that PCR accurately matches units across different treatment groups, effectively mitigates treatment selection bias, and significantly outperforms competitors. Code is available at https://anonymous.4open.science/status/ncr-B697.

翻译：从观测数据中估计异质性处理效应（HTE）因处理选择偏差而面临重大挑战。现有方法通过在潜在空间中最小化处理组间的分布差异来解决此偏差，侧重于全局对齐。然而，局部邻近性这一富有成效的方面——即相似单元往往表现出相似结果——常被忽视。在本研究中，我们提出邻近感知反事实回归（PCR），以在HTE估计背景下利用邻近性进行表征平衡。具体而言，我们引入了一种基于最优传输的局部邻近性保持正则化器，用于在差异计算中刻画局部邻近性。此外，为克服维度灾难——该问题因HTE估计中数据有限而加剧，导致差异估计失效——我们开发了一个信息子空间投影器，该投影器以最小距离精度为代价换取样本复杂度的改善。大量实验表明，PCR能够准确匹配不同处理组间的单元，有效缓解处理选择偏差，并显著优于现有方法。代码可在 https://anonymous.4open.science/status/ncr-B697 获取。