论文标题
预测可以安全地用作在因果一致的贝叶斯广义线性模型中解释的代理
Prediction can be safely used as a proxy for explanation in causally consistent Bayesian generalized linear models
论文作者
论文摘要
贝叶斯建模提供了一种原则性的方法来量化模型参数和模型结构中的不确定性，并且近年来已经看到了大量应用。在贝叶斯工作流程的背景下，我们关注的是模型选择，目的是找到最能解释数据的模型，也就是说，可以帮助我们了解基础数据生成过程。由于我们很少能访问真实过程，因此在现实世界分析期间所剩下的只是从当前数据之外的来源和所述数据预测模型预测的原因。这导致了一个重要的问题，即何时将预测用作模型选择目的解释的代理是有效的。我们通过对贝叶斯通用线性模型的大规模模拟来解决这个问题，在该模型中我们研究了各种因果和统计错误。我们的结果表明，仅当所考虑的模型与真实数据生成过程的基本因果结构完全一致时，将预测用作解释的代理才有效和安全。
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.
