Funnily enough that hardly matters here.
Causality is a largely orthogonal problem to frequentist/bayesian - it makes everything harder, not just one of those!
Causality is a largely orthogonal problem to frequentist/bayesian - it makes everything harder, not just one of those!
If you have a DAG based on wrong assumptions, it doesn't matter whether you get a point estimate based on null hypothesis thinking or whether you get a posterior distribution based on some prior. The problem is that the way in which you combine variables is wrong, and bayesian analysis will just be more detailed and precise in being wrong.
I mean, that's maths, either approach has to give the same results, as they come from the same theory. The Bayes theorem is just a theorem, use it explicitly or not, the numbers will be the same because the axioms are the same.
- bayesian methods give you posterior distributions rather than point estimates and SEs
- bayesian methods natively offer prior and posterior predictive checks
- with bayesian methods, it's evidently easier to combine knowledge from multiple sources, which null-hypothesis testing struggles with (best way is probably still meta-analyses)