  I congratulate Dupont, Wood and Augustin (DWA hereon) for providing an
easy-to-implement method for estimation in the presence of spatial confounding,
and for addressing some of the complicated aspects on the topic. The method
regresses the covariate of interest on spatial basis functions and uses the
residuals of this model in an outcome regression. The authors show that, if the
covariate is not completely spatial, this approach leads to consistent
estimation of the conditional association between the exposure and the outcome.
Below I discuss conceptual and operational issues that are fundamental to
inference in spatial settings: (i) the target quantity and its
interpretability, (ii) the non-spatial aspect of covariates and their relative
spatial scales, and (iii) the impact of spatial smoothing. While DWA provide
some insights on these issues, I believe that the audience might benefit from a
deeper discussion. In what follows, I focus on the setting where a researcher
is interested in interpreting the relationship between a given covariate and an
outcome. I refer to the covariate of interest as the exposure to differentiate
it from the rest.
