Spatial confounding is a fundamental issue in spatial regression models which arises because spatial random eff ects, included to approximate unmeasured spatial variation, are typically not independent of covariates in the model. This can lead to signifi cant bias in covariate eff ect estimates. We develop a broad theoretical framework that brings mathematical clarity to the mechanisms of spatial confounding. Subsequently, we explore the potential of Bayesian methodology in alleviating spatial confounding and leveraging the understanding of how such confounding originates in the construction of prior distributions.