发表机构
Texas A&M University; Shell USA Inc（德克萨斯农工大学; 壳牌美国公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对空间观测研究中处理效应随空间变化且受空间混杂影响的问题，提出图分裂贝叶斯因果森林（GSBCF），融合GS-BART与贝叶斯因果森林框架，通过图引导分裂规则建模空间结构，实现更精确的HTE估计与不确定性量化。
AI 中文摘要
在空间观测研究中，处理分配和结果往往表现出空间依赖模式，且由于已测量和未测量的空间结构化混杂因素，处理效应可能随空间和亚群而变化。在估计异质性处理效应（HTEs）时考虑空间依赖性，是空间因果推断的核心任务。因果贝叶斯加性回归树方法是建模和估计HTEs的流行非参数方法。尽管这些方法具有灵活性和不确定性量化优势，但其常采用的轴对齐分裂规则不适用于建模空间结构。我们提出了一种空间结构感知的贝叶斯非参数方法，称为图分裂贝叶斯因果森林（GSBCF），该方法将图分裂贝叶斯加性回归树（GS-BART）与贝叶斯因果森林倾向得分回归框架相结合，用于空间异质性因果推断。通过在预后函数和HTE函数的决策树建模中采用图引导的分裂规则，来适应空间混杂。我们开发了一种高效的信息化提议采样算法用于后验计算，从而实现对空间条件平均处理效应函数的全贝叶斯推断。模拟实验和真实数据研究表明，与现有因果BART方法相比，该方法在估计精度和不确定性量化方面均有显著提升。
英文摘要
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 and estimating HTEs. Despite their flexibility and uncertainty quantification, the axis-aligned split rules often adopted in these models are not suitable for modeling spatial structures. We propose a spatial structure-aware Bayesian nonparametric method, called Graph-Split Bayesian Causal Forest (GSBCF), that integrates graph-split Bayesian additive regression trees (GS-BART) with the Bayesian causal forest propensity-score regression framework for spatial heterogeneous causal inference. Spatial confounding is accommodated through graph-guided split rules in modeling decision trees of the prognostic and HTE functions. We develop an efficient informed proposal sampling algorithm for posterior computation, enabling full Bayesian inference of the spatial conditional average treatment effect function. Simulations and a real data study demonstrate substantially improved estimation accuracy and uncertainty quantification over existing causal BART methods.
