Heterogeneous Treatment Effects in Panel Data

We address a core problem in causal inference: estimating heterogeneous treatment effects (HTEs) using panel data with general treatment patterns. Many existing methods either do not utilize the potential underlying structure in panel data or have limitations in the allowable treatment patterns. In this work, we propose and evaluate a new method that first partitions observations into disjoint clusters with similar treatment effects using a regression tree, and then leverages the underlying structure of the panel data to estimate the average treatment effect (ATE) for each cluster. Computation experiments with semi-synthetic data show that our method achieves superior accuracy for ATE and HTE estimation compared to alternative approaches. This performance was achieved using a regression tree with no more than 40 leaves, making the method both accurate and interpretable, and a strong candidate for practical applications.