It also applies to standardization. So, we show that simple models without random effects are not guaranteed sufficient to account for clustering in non-linear models, and especially when estimating counterfactual means (even in linear models).

Our paper focuses on AIPW. We show when there will be low coverage due to clustering (by center). You can account for it using fixed effects or random effects. However, fixed effects have bad performance due to overfitting when there are many small centers, which was the case in our example