@peder_isager It is a very different marginal landscape. In the first, p(C), A, and B are marginally dependent. In the second they are marginally independent; It’s as if they have all been experimentally manipulated. Your question becomes, essentially, “why do colliders block paths”
@AdanBecerraPhD @SolomonKurz@stephenjwild For example, Figure 2c shows a confounder pointing to the interaction effect only, but not to G or E. So in that case how would one determine rules for when they can identify the effect of E on D?
@SolomonKurz @AdanBecerraPhD @stephenjwild DAGs are non-parametric. The just represent which variables enter into functions determining other variables. The nature of those functions (e.g. interactions, linearity, etc) are not represented.
@sTeamTraen Can't speak to how they would have calculated it. But see e.g. the blurb in the effectsize package below. Easy enough to do by hand, in any case.
@AshleyJ_Thomas Not sure if there is a specific option. But in general you'd want to be careful testing everything possible. Everything may not be equally interpretable.
Every statistician will come to a point in their career where they feel like they have no choice but to get a tattoo with "what is your research question?" on their forearm
Teaching evaluations are a funny thing.
"Alexander was not a professor, he just regurgitated info on slides."
I don't even teach from slides in that course🤷♂️