Causal inference in spatial settings is met with unique challenges and opportunities. In spatial settings, a unit's outcome might be affected by the exposure at many locations and the confounders might be spatially structured. Using causal diagrams, we investigate the complications that arise when investigating causal relationships from spatial data. We illustrate that spatial confounding and interference can manifest as each other, meaning that investigating the presence of one can lead to wrongful conclusions in the presence of the other. We also show that statistical dependencies in the exposure can render standard analyses invalid, which can have crucial implications for understanding the effect of interventions on dependent units. Based on the conclusions from this investigation, we propose a parametric approach that simultaneously accounts for interference and mitigates bias from local and neighborhood unmeasured spatial confounding. We show that incorporating an exposure model is necessary from a Bayesian perspective. Therefore, the proposed approach is based on modeling the exposure and the outcome simultaneously while accounting for the presence of common spatially-structured unmeasured predictors. We illustrate our approach with a simulation study and with an analysis of the local and interference effects of sulfur dioxide emissions from power plants on cardiovascular mortality.

翻译：在空间设定中进行因果推断面临着独特的挑战与机遇。在空间场景中，一个单元的结果可能受到多个位置暴露的影响，而混杂因素可能具有空间结构。利用因果图，我们研究了从空间数据探究因果关系时出现的复杂问题。我们阐明了空间混杂与干扰可能相互表现，这意味着在存在其中一者的情况下探究另一者的存在可能导致错误的结论。我们还指出，暴露中的统计依赖性可能使标准分析失效，这对理解干预措施对依赖单元的影响具有关键意义。基于本研究的结论，我们提出了一种参数化方法，该方法同时考虑干扰并减轻来自局部和邻域未测量空间混杂的偏差。研究表明，从贝叶斯视角出发，纳入暴露模型是必要的。因此，所提出的方法基于对暴露与结果的联合建模，同时考虑共有的空间结构未测量预测因子的存在。我们通过模拟研究以及分析燃煤电厂二氧化硫排放对心血管疾病死亡率的局部与干扰效应，演示了该方法。