This paper introduces a novel approach for estimating heterogeneous treatment effects of binary treatment in panel data, particularly focusing on short panel data with large cross-sectional data and observed confoundings. In contrast to traditional literature in difference-in-differences method that often relies on the parallel trend assumption, our proposed model does not necessitate such an assumption. Instead, it leverages observed confoundings to impute potential outcomes and identify treatment effects. The method presented is a Bayesian semi-parametric approach based on the Bayesian causal forest model, which is extended here to suit panel data settings. The approach offers the advantage of the Bayesian approach to provides uncertainty quantification on the estimates. Simulation studies demonstrate its performance with and without the presence of parallel trend. Additionally, our proposed model enables the estimation of conditional average treatment effects, a capability that is rarely available in panel data settings.

翻译：本文提出了一种新颖的方法，用于估计面板数据中二元处理的异质性处理效应，特别关注具有大截面数据和观测混杂因素的短面板数据。与通常依赖平行趋势假设的传统双重差分法文献不同，我们提出的模型无需此类假设。相反，它利用观测到的混杂因素来估算潜在结果并识别处理效应。所提出的方法是一种基于贝叶斯因果森林模型的贝叶斯半参数方法，本文将其扩展以适应面板数据设置。该方法具有贝叶斯方法的优势，能够为估计提供不确定性量化。模拟研究展示了其在存在与不存在平行趋势情况下的性能。此外，我们提出的模型能够估计条件平均处理效应，这一能力在面板数据设置中较为罕见。