The study of causal effects in the presence of unmeasured spatially varying confounders has garnered increasing attention. However, a general framework for identifiability, which is critical for reliable causal inference from observational data, has yet to be advanced. In this paper, we study a linear model with various parametric model assumptions on the covariance structure between the unmeasured confounder and the exposure of interest. We establish identifiability of the treatment effect for many commonly 20 used spatial models for both discrete and continuous data, under mild conditions on the structure of observation locations and the exposure-confounder association. We also emphasize models or scenarios where identifiability may not hold, under which statistical inference should be conducted with caution.

翻译：在存在未测量空间变异混杂因子的情况下研究因果效应已引起越来越多的关注。然而，可识别性的一般框架——这对于从观测数据中进行可靠的因果推断至关重要——尚未得到推进。本文研究了一个线性模型，该模型对未测量混杂因子与感兴趣暴露之间的协方差结构设定了多种参数模型假设。我们在观测位置结构及暴露-混杂关联的温和条件下，为离散和连续数据中许多常用的空间模型建立了处理效应的可识别性。同时，我们也强调了可识别性可能不成立的模型或场景，在此类情况下进行统计推断需格外谨慎。