Graph-Split Bayesian Causal Forest for Spatial Heterogeneous Treatment Effect Estimation
图分割贝叶斯因果森林:空间异质性处理效应估计
In spatial observational studies, treatment assignment and outcomes often exhibit spatial dependence patterns, and treatment effects may vary across space and subpopulations due to both measured and unmeasured spatially structured confounders. Accounting for spatial dependence while estimating heterogeneous treatment effects (HTEs) is a central task in spatial causal inference. Causal Bayesian additive regression tree methods are popular nonparametric methods for modeling…