Gaussian process-based models are attractive for estimating heterogeneous treatment effects (HTE), but their computational cost limits scalability in causal inference settings. In this work, we address this challenge by extending Patchwork Kriging into the causal inference framework. Our proposed method partitions the data according to the estimated propensity score and applies Patchwork Kriging to enforce continuity of HTE estimates across adjacent regions. By imposing continuity constraints only along the propensity score dimension, rather than the full covariate space, the proposed approach substantially reduces computational cost while avoiding discontinuities inherent in simple local approximations. The resulting method can be interpreted as a smoothing extension of stratification and provides an efficient approach to HTE estimation. The proposed method is demonstrated through simulation studies and a real data application.

翻译：基于高斯过程的模型在估计异质处理效应方面具有吸引力，但其计算成本限制了在因果推断场景中的可扩展性。本研究通过将拼凑克里金法扩展至因果推断框架来解决这一挑战。所提出的方法根据估计的倾向性得分对数据进行分区，并应用拼凑克里金法来确保相邻区域间异质处理效应估计的连续性。通过仅在倾向性得分维度而非整个协变量空间施加连续性约束，该方法在避免简单局部近似固有间断性的同时，显著降低了计算成本。所得方法可解释为分层方法的平滑扩展，并为异质处理效应估计提供了高效途径。通过模拟研究和实际数据应用验证了所提出方法的有效性。