Bringing spatial confounding into the causal inferential fold

Spatial patterning of environmental hazards often leads to concerns about spatial confounding: that the exposures we study share similar spatial distributions with other causes of disease. Recent efforts to address spatial confounding have approached it using clever specification of spatial models, or models that adjust for aspects of spatial location itself. In the article by Li et al. (Am J Epidemiol. 2025; kwaf248), the authors describe and demonstrate several models for addressing spatial confounding for binary exposures. These important results demonstrate an aspect of environmental exposures that should concern all environmental epidemiologists: inadequate adjustment for spatial confounding can increase, rather than decrease, bias from spatial confounding. In this commentary, we disagree with some terminology and enthusiastically agree with the importance of the problem and the utility of the approaches described by these authors. Causal inference in environmental epidemiology is fraught with many challenges, and Li et al. give hope for progress on one of the lesser understood, yet potentially ubiquitous, problems: spatial confounding.