Heterogeneous Synthetic Learner for Panel Data

TL;DR

This paper introduces the Heterogeneous Synthetic Learner for panel data, addressing the challenge of estimating individualized treatment effects in non-stationary, dependent data settings, and demonstrating its effectiveness through theoretical and empirical results.

Contribution

It develops novel HTE estimators for panel data that handle heterogeneity, non-stationarity, and temporal dependence, filling a significant gap in existing methods.

Findings

Proposed H1SL and H2SL estimators with proven convergence rates.

Demonstrated superior performance over existing methods in numerical studies.

Extended synthetic control methods to heterogeneous treatment effect estimation.

Abstract

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.