Heterogeneous Synthetic Learner for Panel Data
This addresses a gap in personalized treatment effect estimation for applications involving panel data, representing an incremental advancement by combining existing HTE estimators with synthetic control techniques.
The paper tackles the problem of estimating heterogeneous treatment effects (HTE) in panel data, which existing methods fail to handle due to non-stationarity and temporal dependencies, by proposing H1SL and H2SL estimators that generalize synthetic control methods, achieving superior performance in numerical studies.
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.