I’m wondering how waic and loo operate when applied to multivariate models. Specifically, I’m interested in computing and scoring bayesian networks or path models using brms. For instance, asking whether three variables A, B, and C are better explained by a causal chain structure, e.g. A --> B --> C or by a common cause structure, e.g. A <-- B --> C. In the first model, the “response” variables are B and C, with A only acting as a predictor. In the second model, the response variables are A and C, with B only acting as a predictor. In brms formulas:
model1 = mvbf(B ~ A, C ~ B)
model2 = mvbf(A ~ B, C ~ B)
Will I be able to meaningfully compare these models by waic or loo (setting aside concerns about inferring causal relationships)? I ask because, if I were to use the predict() function, brms would be happy to predict B from A in the first model, but it would take more work to do that in the second model. I have it in my head waic and loo internally depend on predict(), but maybe that’s wrong.
I’ve looked at the multivariate vignette where loo is used, but there the models being compared always concern the same response variables.
- brms Version: 2.2