Heterogeneous treatment effect (HTE) estimation plays a crucial role in developing personalized treatment plans across various applications. Conventional approaches assume that the observed data are independent and identically distributed (i.i.d.). In some real applications, however, the assumption does not hold: the environment may evolve, which leads to variations in HTE over time. To enable HTE estimation in evolving environments, we introduce and formulate the online HTE estimation problem. We propose an online ensemble-based HTE estimation method called ETHOS, which is capable of adapting to unknown evolving environments by ensembling the outputs of multiple base estimators that track environmental changes at different scales. Theoretical analysis reveals that ETHOS achieves an optimal expected dynamic regret \(O(\sqrt{T(1+P_T)})\), where T denotes the number of observed examples and \(P_T\) characterizes the intensity of environment changes. The achieved dynamic regret ensures that our method consistently approaches the optimal online estimators as long as the evolution of the environment is moderate. We conducted extensive experiments on three common benchmark datasets with various environment evolving mechanisms. The results validate the theoretical analysis and the effectiveness of our proposed method.

异质治疗效果 (HTE) 估计在跨各种应用制定个性化治疗计划方面发挥着至关重要的作用。传统方法假设观察到的数据是独立同分布（iid）的。然而，在一些实际应用中，这一假设并不成立：环境可能会发生变化，从而导致 HTE 随着时间的推移而发生变化。为了在不断变化的环境中实现 HTE 估计，我们引入并制定了在线 HTE 估计问题。我们提出了一种称为 ETHOS 的基于集成的在线 HTE 估计方法，该方法能够通过集成跟踪不同尺度环境变化的多个基本估计器的输出来适应未知的不断变化的环境。理论分析表明，ETHOS 实现了最优的预期动态遗憾\(O(\sqrt{T(1+P_T)})\)，其中T表示观察到的样本数量，\(P_T\)表征环境变化的强度。只要环境的演变是适度的，所实现的动态遗憾确保我们的方法始终接近最佳在线估计器。我们对具有各种环境演化机制的三个常见基准数据集进行了广泛的实验。结果验证了理论分析和我们提出的方法的有效性。