A unified framework for estimating direct causal effect under spatial confounding and interference, with the R package spaci
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AI 根据所提供的论文材料归类：摘要直接研究观测数据中的因果效应识别，并将邻近单位的空间干扰纳入估计框架，对应因果推断及网络与干扰。
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空间混杂与干扰下直接因果效应估计的统一框架及 R 包 spaci
- 研究问题
- 当未观测空间因素与邻近单位之间的干扰同时存在时，如何定义、识别并估计直接因果效应，同时正确处理空间相关带来的推断问题？
- 方法与思路
- 作者提出结合邻域暴露和空间距离的 iDAPS 匹配，以及扩充混杂成分与暴露的 recoverU+ 估计，并配套空间 HAC 等推断方法。
- 材料陈述的贡献
- 摘要称给出识别结果、匹配偏差界及含残余偏差的双重稳健结果，并开发 spaci 包；臭氧应用未发现相关技术降低臭氧的证据。
- 阅读关注点
- 应核对直接效应的暴露定义、识别条件、混杂成分恢复要求与不可恢复部分的偏差，以及空间标准误和重抽样方法的校准条件。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收未核实；摘要未说明完整识别条件、数值结果及应用的不确定性，不能把未发现证据解释为效应为零。
原文摘要
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.