Disentangling spatial interference and spatial confounding biases in causal inference
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区分因果推断中的空间干扰与空间混杂偏差
- 研究问题
- 在空间观测数据中，如何区分干扰、直接混杂及间接混杂对因果估计的偏差，并理解空间权重和暴露分布的作用？
- 方法与思路
- 作者从有向无环图出发澄清空间混杂，用泊松分布等例子放宽既有正态设定，推导不同空间机制所产生的解析偏差表达式。
- 材料陈述的贡献
- 摘要称，空间权重、治疗分布与干扰强度共同决定偏差，直接和间接空间混杂可加以区分，并以模拟和真实数据支持理论分析。
- 阅读关注点
- 应核对图结构与各类偏差的对应关系、非正态推导的适用范围，以及空间权重选择如何影响直接效应和间接效应的解释。
- 阅读边界
- 这是预印本，同行评审或期刊接收未核实。摘要未说明全部分布条件；同时调整各类混杂与干扰的参数框架仍被列为未来工作。
原文摘要
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.