发表机构
University of Manchester; Federal University of Agriculture, Abeokuta（曼彻斯特大学; 阿贝奥库塔联邦农业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出统一框架同时处理空间混杂和干扰，通过iDAPS匹配和recoverU+双重稳健估计器估计直接因果效应，并验证于SCR/SNCR臭氧影响。
AI 中文摘要
由于缺乏可交换性，使用观测数据估计因果效应具有挑战性，而空间数据进一步增加了复杂性。在农业和环境研究等实际应用中，未观测到的空间因素（空间混杂，SC）和邻近位置单元的相互作用（空间干扰，SI）通常同时出现，然而现有的参数方法分别处理它们。我们开发了一个同时处理两者的统一框架。我们首先精确定义估计量并给出识别结果。然后我们研究两种估计器。匹配估计器iDAPS将邻域暴露和空间邻近性整合到倾向得分匹配中，并采用平衡优化的权重。我们证明了一个变异函数偏差界，表明其混杂偏差受匹配距离处混杂因子空间变异性的控制；这证明了复合度量的合理性，得出一致性结果，并提供可计算的诊断。双重稳健估计器recoverU+用恢复的混杂因子和暴露增强倾向得分和结果模型；我们证明它在显式的不可恢复混杂因子成分的残余偏差内是双重稳健的。我们表明基于独立性的标准误差在空间依赖下是反保守的；我们提供空间异方差和自相关一致（HAC）、块自助法和基于随机化的替代方案，并验证其校准。每个理论结果都经过数值验证。估计选择性催化和非催化还原（SCR/SNCR）技术对环境臭氧的影响显示没有臭氧减少的证据，而朴素分析会给出自信的错误符号。所有方法都在开发的开源R包spaci中实现。
英文摘要
Estimating causal effects is challenging with observational data due to the lack of exchangeability, and is further complicated with spatial data. In real-life applications such as agricultural and environmental studies, unobserved spatial factors (spatial confounding, SC) and interactions of units in nearby locations (spatial interference, SI) commonly occur jointly, yet existing parametric methods address them separately. We develop a unified framework that treats both simultaneously. We first define the estimand precisely and give an identification result. We then study two estimators. The matching estimator iDAPS integrates neighbourhood exposure and spatial proximity into propensity score matching with balance-optimised weights. We prove a variogram bias bound showing its confounding bias is controlled by the spatial variability of the confounder at the matched distance; this justifies the composite metric, yields a consistency result, and provides a computable diagnostic. The doubly robust estimator recoverU+ augments the propensity and outcome models with the recovered confounder and the exposure; we prove it is doubly robust up to an explicit residual bias from the unrecoverable confounder component. We show that independence-based standard errors are anticonservative under spatial dependence; we provide spatial heteroskedasticity and autocorrelation consistent (HAC), block-bootstrap, and randomisation-based alternatives whose calibration we verify. Every theoretical result is validated numerically. Estimating the effect of selective catalytic and non-catalytic reduction (SCR/SNCR) technologies on ambient ozone reveals no evidence of ozone reduction, while a naive analysis would have delivered a confidently wrong sign. All methods are implemented in the developed open-source R package spaci.
