Spatial confounding is a persistent challenge in spatial statistics, influencing the validity of statistical inference in models that analyze spatially-structured data. The concept has been interpreted in various ways but is broadly defined as bias in estimates arising from unmeasured spatial variation. In this paper we review definitions, classical spatial models, and recent methodological advances, including approaches from spatial statistics and causal inference. We provide an unified view of the many available approaches for areal as well as geostatistical data and discuss their relative merits both theoretically and empirically with a head-to-head comparison on real datasets. Finally, we leverage the results of the empirical comparisons to discuss directions for future research.

翻译：空间混杂是空间统计学中一个持续存在的挑战，它影响着分析空间结构化数据的模型中统计推断的有效性。这一概念存在多种解释方式，但广义上被定义为由未测量的空间变异引起的估计偏差。本文回顾了相关定义、经典空间模型以及最新的方法学进展，包括来自空间统计学和因果推断的方法。我们为面状数据和地统计数据的多种现有方法提供了统一视角，并通过理论分析和实证比较（在真实数据集上进行直接对比）讨论了它们的相对优劣。最后，我们基于实证比较的结果，探讨了未来研究的方向。