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性处理效应（HTE）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。本研究通过将拼图克里金法扩展到因果推断框架中来应对这一挑战。所提方法根据估计的倾向得分对数据进行分区，并应用拼图克里金法强制相邻区域的HTE估计具有连续性。通过仅在倾向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的不连续性同时，显著降低了计算成本。所得方法可解释为分层法的平滑扩展，并为HTE估计提供了高效途径。通过模拟研究和实际数据应用对所提方法进行了验证。