Bayesian modeling provides a principled approach to quantifying uncertainty in model parameters and model structure 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, that is, help us understand the underlying data generating process. Since we rarely have access to the true process, all we are left with during real-world analyses is incomplete causal knowledge from sources outside of the current data and model predictions of said 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 only when the models under consideration are sufficiently consistent with the underlying causal structure of the true data generating process.

翻译：贝叶斯建模为量化模型参数和模型结构中的不确定性提供了原则性方法，近年来其应用激增。在贝叶斯工作流程的背景下，我们关注模型选择，旨在寻找最能解释数据（即帮助我们理解底层数据生成过程）的模型。由于我们很少能接触真实过程，实际分析中仅能依赖源自当前数据之外的外部因果知识及对这些数据的模型预测。这引出一个重要问题：何时将预测作为解释的代理用于模型选择是可行的。我们通过大规模贝叶斯广义线性模型仿真来探讨此问题，研究各种因果和统计错误设定。结果表明，仅当所考虑的模型与真实数据生成过程的底层因果结构充分一致时，将预测作为解释的代理才是有效且安全的。