Heterogeneous Treatment Effect Estimation with Distribution Shift and Unmeasured Confounding

Abstract

Heterogeneous treatment effect (HTE) estimation is vital for understanding how treatment effects vary across individuals or subgroups. Recently, numerous HTE models, such as balancing methods and instrumental variable (IV) methods, have been developed to eliminate confounding bias from both observed variables and unmeasured confounders. However, many of these models overlook distribution shifts across populations, limiting their applicability to in-distribution (ID) scenarios, where the sample distribution matches that of the training dataset. In real-world applications where population distributions are subject to continuous change, there is an urgent need to develop methods for stable HTE estimation across out-of-distribution (OOD) populations. As pioneers in resolving this problem, we propose a novel Stable Balanced Representation Learning with Hierarchical-Attention Paradigm and Instrumental Regularizer (SBRL-HAPI) framework, which consists of four key components: 1) Instrumental Regularizer (IvR) for mitigating unmeasured confounding, 2) Balancing Regularizer (BR) for eliminating confounding bias induced by observed variables, 3) Independence Regularizer (IR) for addressing the distribution shift and 4) Hierarchical-Attention Paradigm (HAP) for coordination between balance and independence. In this way, SBRL-HAPI 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 on synthetic and semi-synthetic datasets demonstrate the effectiveness of all four modules including IvR in achieving stable HTE estimation. Furthermore, large-scale industrial experiments on the Kuaishou dataset confirm the OOD robustness of BR, IR, and HAP under realistic distribution shifts.