Prediction can be safely used as a proxy for explanation in causally consistent Bayesian generalized linear models

Abstract

Bayesian modeling provides a principled approach to quantifying uncertainty and has seen a surge of applications in recent years. Within the context of a Bayesian workflow, we are concerned with model selection for the purpose of finding models that best explain the data or underlying data generating process. Since insight into the true process is rare, what remains is incomplete causal knowledge and model predictions of the data. This leads to the important question of when the use of prediction as a proxy for explanation for the purpose of model selection is valid. We approach this question by means of large-scale simulations of Bayesian generalized linear models where we investigate various causal and statistical misspecifications. Our results indicate that the use of prediction as proxy for explanation is valid and safe if the models under consideration are sufficiently consistent with the underlying causal structure of the true data generating process.