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：在个性化分析的新时代，学习异质处理效应（HTE）已成为必然趋势，并在众多领域得到广泛应用。然而，现有大多数HTE估计方法聚焦于独立同分布观测数据，无法应对常见面板数据中的非平稳性和时间依赖性。另一方面，为面板数据开发的处理效应评估方法通常忽略个体化信息。为弥补这一空白，本文首次系统研究面板数据中的HTE估计问题。针对HTE可识别性的不同假设，我们利用非面板数据中最先进的HTE估计器，并推广允许灵活数据生成过程的合成控制方法，提出两种异质合成学习器——H1SL（异质单边合成学习器）与H2SL（异质双边合成学习器）。我们建立了所提估计量的收敛速率，并通过大量数值实验验证了新方法相较现有方法的优越性能。