In the last two decades, considerable research has been devoted to a phenomenon known as spatial confounding. Spatial confounding is thought to occur when there is multicollinearity between a covariate and the random effect in a spatial regression model. This multicollinearity is considered highly problematic when the inferential goal is estimating regression coefficients and various methodologies have been proposed to attempt to alleviate it. Recently, it has become apparent that many of these methodologies are flawed, yet the field continues to expand. In this paper, we offer a novel perspective of synthesizing the work in the field of spatial confounding. We propose that at least two distinct phenomena are currently conflated with the term spatial confounding. We refer to these as the ``analysis model'' and the ``data generation'' types of spatial confounding. We show that these two issues can lead to contradicting conclusions about whether spatial confounding exists and whether methods to alleviate it will improve inference. Our results also illustrate that in most cases, traditional spatial linear mixed models do help to improve inference on regression coefficients. Drawing on the insights gained, we offer a path forward for research in spatial confounding.

翻译：过去二十年中，大量研究致力于探讨被称为空间混杂的现象。当空间回归模型中协变量与随机效应存在多重共线性时，即被认为发生了空间混杂。当推断目标是估计回归系数时，这种多重共线性被视为高度问题化，学界已提出多种方法来试图缓解该问题。近期研究表明，这些方法中许多存在缺陷，但该领域仍在持续扩展。本文提出整合空间混杂领域研究的新视角，指出当前被统称为"空间混杂"的术语实际上至少混淆了两种不同现象。我们将这两种现象分别称为"分析模型型"与"数据生成型"空间混杂。研究表明，这两类问题可能导致关于空间混杂是否存在、以及缓解方法是否能改进推断的相互矛盾的结论。研究结果还表明，在多数情况下，传统空间线性混合模型确实有助于改进回归系数的推断。基于所得见解，我们为空间混杂研究的未来发展提出了路径建议。