Scalable Heterogeneous Treatment Effect Estimation with Propensity Patchwork Kriging

This paper addresses the computational limitations of Gaussian process-based models for estimating heterogeneous treatment effects (HTE) in causal inference. It proposes a novel method, Propensity Patchwork Kriging, which leverages the propensity score to partition the data and apply Patchwork Kriging. This approach aims to improve scalability while maintaining the accuracy of HTE estimates by enforcing continuity constraints along the propensity score dimension. The method offers a smoothing extension of stratification, making it an efficient approach for HTE estimation.