In the new era of personalization, learning the heterogeneous treatment effect (HTE) becomes an inevitable trend with numerous applications. Yet, most existing HTE estimation methods focus on independently and identically distributed observations and cannot handle the non-stationarity and temporal dependency in the common panel data setting. The treatment evaluators developed for panel data, on the other hand, typically ignore the individualized information. To fill the gap, in this paper, we initialize the study of HTE estimation in panel data. Under different assumptions for HTE identifiability, we propose the corresponding heterogeneous one-side and two-side synthetic learner, namely H1SL and H2SL, by leveraging the state-of-the-art HTE estimator for non-panel data and generalizing the synthetic control method that allows flexible data generating process. We establish the convergence rates of the proposed estimators. The superior performance of the proposed methods over existing ones is demonstrated by extensive numerical studies.

翻译：在新的个性化时代,学习多种处理效应(HTE)成为许多应用的不可避免的趋势。然而,大多数现有的HTE估计方法侧重于独立和分布相同的观测,无法处理共同小组数据设置中的非常态和时间依赖性。为小组数据开发的治疗评价人员通常忽视个人化信息。为了填补空白,我们在本文件中在小组数据中开始研究HTE估计。在对HTE可识别性的不同假设下,我们提出相应的单方和双方合成学习者(即H1SL和H2SL),办法是利用最新的HTE估计数据用于非小组数据,并推广综合控制方法,允许灵活的数据生成过程。我们建立了拟议估算者的聚合率。拟议的方法优于现有方法的优异性表现通过广泛的数字研究得到证明。