  We address a core problem in causal inference: estimating heterogeneous
treatment effects 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 (assumed) low-rank structure of the panel data to
estimate the average treatment effect for each cluster. Our theoretical results
establish the convergence of the resulting estimates to the true treatment
effects. Computation experiments with semi-synthetic data show that our method
achieves superior accuracy compared to alternative approaches, using a
regression tree with no more than 40 leaves. Hence, our method provides more
accurate and interpretable estimates than alternative methods.
