Causal inference in spatial settings is met with unique challenges and opportunities. On one hand, a unit's outcome can be affected by the exposure at many locations, leading to interference. On the other hand, unmeasured spatial variables can confound the effect of interest. Our work has two overarching goals. First, using causal diagrams, 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, and that statistical dependencies in the exposure variable can render standard analyses invalid. This can have crucial implications for analyzing data with spatial or other dependencies, and for understanding the effect of interventions on dependent units. Secondly, we propose a parametric approach to mitigate bias from local and neighborhood unmeasured spatial confounding and account for interference simultaneously. This approach is based on simultaneous modeling of the exposure and the outcome while accounting for the presence of spatially-structured unmeasured predictors of both variables. 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：空间情境下的因果推断面临独特的挑战与机遇。一方面，单元结局可能受到多个位置暴露的影响，从而产生交互作用。另一方面，未测量的空间变量会混淆目标效应的估计。本研究聚焦两个核心目标：首先，通过因果图揭示空间混杂与交互作用可能相互表征，即研究其中之一可能导致另一存在时的错误结论，且暴露变量的统计依赖性会使得标准分析失效。这一发现对分析具有空间或其他依赖性的数据、理解干预对相依单元的影响具有关键意义。其次，我们提出一种参数化方法，可同时减轻局域与邻域未测量空间混杂导致的偏倚，并处理交互作用。该方法通过同步建模暴露与结局变量，同时考虑两者存在空间结构性未测量预测因子的情况。我们通过模拟研究及电厂二氧化硫排放对心血管疾病死亡率的局域与交互效应分析，验证了该方法的有效性。