The scientific rigor and computational methods of causal inference have had great impacts
on many disciplines but have only recently begun to take hold in spatial applications. Spatial …

In spatial regression models, collinearity between covariates and spatial effects can lead to
significant bias in effect estimates. This problem, known as spatial confounding, is …

The concept of spatial confounding is closely connected to spatial regression, although no
general definition has been established. A generally accepted idea of spatial confounding in …

Spatial models are used in a variety of research areas, such as environmental sciences,
epidemiology, or physics. A common phenomenon in such spatial regression models is …

1) Species distribution models (SDMs) are currently the main tools to derive species niche
estimates and spatially explicit predictions for species geographical distribution. However …

The issue of spatial confounding between the spatial random effect and the fixed effects in
regression analyses has been identified as a concern in the statistical literature. Multiple …

Spatial confounding, that is, collinearity between fixed effects and random effects in a spatial
generalized linear mixed model, can adversely affect estimates of the fixed effects …

Adjusting for an unmeasured confounder is generally an intractable problem, but in the
spatial setting it may be possible under certain conditions. We derive necessary conditions …

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 collinearity …

Assessing associations between a response of interest and a set of covariates in spatial
areal models is the leitmotiv of ecological regression. However, the presence of spatially …