Spatial interference and spatial confounding are two major issues inhibiting precise causal estimates when dealing with observational spatial data. Moreover, the definition and interpretation of spatial confounding remain arguable in the literature. In this paper, our goal is to provide clarity in a novel way on misconception and issues around spatial confounding from Directed Acyclic Graph (DAG) perspective and to disentangle both direct, indirect spatial confounding and spatial interference based on bias induced on causal estimates. Also, existing analyses of spatial confounding bias typically rely on Normality assumptions for treatments and confounders, assumptions that are often violated in practice. Relaxing these assumptions, we derive analytical expressions for spatial confounding bias under more general distributional settings using Poisson as example . We showed that the choice of spatial weights, the distribution of the treatment, and the magnitude of interference critically determine the extent of bias due to spatial interference. We further demonstrate that direct and indirect spatial confounding can be disentangled, with both the weight matrix and the nature of exposure playing central roles in determining the magnitude of indirect bias. Theoretical results are supported by simulation studies and an application to real-world spatial data. In future, parametric frameworks for concomitantly adjusting for spatial interference, direct and indirect spatial confounding for both direct and mediated effects estimation will be developed.

翻译：在处理观测空间数据时，空间干扰与空间混杂是阻碍精确因果估计的两大主要问题。此外，空间混杂的定义与解释在现有文献中仍存在争议。本文旨在从有向无环图（DAG）的视角出发，以新颖方式澄清关于空间混杂的误解及相关问题，并基于因果估计中引入的偏差，对直接与间接空间混杂以及空间干扰进行解耦。现有对空间混杂偏差的分析通常依赖于处理变量与混杂变量的正态性假设，而这些假设在实践中常被违反。通过放宽这些假设，我们以泊松分布为例，推导了更一般分布设定下空间混杂偏差的解析表达式。研究表明，空间权重矩阵的选择、处理变量的分布以及干扰的强度，共同决定了由空间干扰引起的偏差程度。我们进一步证明，直接与间接空间混杂可以被区分解耦，其中权重矩阵与暴露变量的性质在决定间接偏差大小中起核心作用。理论结果得到了仿真研究及实际空间数据应用的支持。未来，我们将开发参数化框架，以同时调整空间干扰、直接与间接空间混杂，用于直接效应与中介效应的估计。