I really liked this paper. It's great when someone so clearly and thoroughly writes about the pitfalls of believing a better procedure will cure inherently theoretical issues with analyses or study design.
A similar version of this comes in from King's "How Robust Standard Errors Expose Methodological Problems They Do Not Fix, and What to Do About It" from 2014's Political Analysis, and I feel matters in the same way that Geographical Analysis & Spatial Statistics cannot simply be about fixing specifications to account for correlated error.
What's your theory for why the misspecification exists? Why is the error correlated? Is there a way you can directly account for that fact (e.g. competing destinations from interaction modelling) instead of applying a theory-free treatment for it?
I'm not sure if spatial confouding & corrections are necessarily as fundamental as the principles behind RCT, but I'm sure that, as statistics struggles to define itself alongside (or inside of) data science, spatial stats will have to carve a similar niche.