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与模型结构的不确定性提供了理论严谨的方法，近年来其应用大幅增长。在贝叶斯工作流框架下，我们关注的是模型选择问题，旨在寻找最能解释数据（即帮助理解潜在数据生成过程）的模型。由于真实数据生成过程难以获知，实际分析中我们只能依赖当前数据之外的先验因果知识与模型预测。这引出一个关键问题：何时使用预测作为模型选择的解释替代指标是合理的？我们通过大规模模拟贝叶斯广义线性模型来探讨该问题，系统考察了各类因果结构与统计模型误设。研究结果表明：仅当所考虑的模型与真实数据生成过程的潜在因果结构保持充分一致时，将预测作为解释的替代指标才是有效且安全的。