Heterogeneous treatment effect (HTE) estimation is vital for understanding the change of treatment effect across individuals or subgroups. Most existing HTE estimation methods focus on addressing selection bias induced by imbalanced distributions of confounders between treated and control units, but ignore distribution shifts across populations. Thereby, their applicability has been limited to the in-distribution (ID) population, which shares a similar distribution with the training dataset. In real-world applications, where population distributions are subject to continuous changes, there is an urgent need for stable HTE estimation across out-of-distribution (OOD) populations, which, however, remains an open problem. As pioneers in resolving this problem, we propose a novel Stable Balanced Representation Learning with Hierarchical-Attention Paradigm (SBRL-HAP) framework, which consists of 1) Balancing Regularizer for eliminating selection bias, 2) Independence Regularizer for addressing the distribution shift issue, 3) Hierarchical-Attention Paradigm for coordination between balance and independence. In this way, SBRL-HAP regresses counterfactual outcomes using ID data, while ensuring the resulting HTE estimation can be successfully generalized to out-of-distribution scenarios, thereby enhancing the model's applicability in real-world settings. Extensive experiments conducted on synthetic and real-world datasets demonstrate the effectiveness of our SBRL-HAP in achieving stable HTE estimation across OOD populations, with an average 10% reduction in the error metric PEHE and 11% decrease in the ATE bias, compared to the SOTA methods.

翻译：异质性处理效应估计对于理解处理效应在个体或亚组间的变化至关重要。现有的大多数HTE估计方法侧重于解决由处理组和对照组之间混杂因子分布不平衡引起的选择偏差，但忽略了跨人群的分布偏移。因此，这些方法的适用性仅限于与训练数据集具有相似分布的内分布人群。在现实应用中，人群分布持续变化，亟需能够跨外分布人群进行稳定HTE估计的方法，然而这仍是一个未解决的开放性问题。作为解决该问题的先驱，我们提出了一种新颖的基于分层注意力范式的稳定平衡表征学习框架，该框架包含：1) 用于消除选择偏差的平衡正则化器；2) 用于解决分布偏移问题的独立性正则化器；3) 用于协调平衡性与独立性的分层注意力范式。通过这种方式，SBRL-HAP利用ID数据回归反事实结果，同时确保所得的HTE估计能够成功推广到外分布场景，从而增强模型在现实环境中的适用性。在合成和真实数据集上进行的大量实验表明，我们的SBRL-HAP在跨OOD人群实现稳定HTE估计方面具有有效性，与最先进方法相比，其误差度量PEHE平均降低了10%，ATE偏差平均减少了11%。