The concepts of Bayesian prediction, model comparison, and model selection have developed significantly over the last decade. As a result, the Bayesian community has witnessed a rapid growth in theoretical and applied contributions to building and selecting predictive models. Projection predictive inference in particular has shown promise to this end, finding application across a broad range of fields. It is less prone to over-fitting than na\"ive selection based purely on cross-validation or information criteria performance metrics, and has been known to out-perform other methods in terms of predictive performance. We survey the core concept and contemporary contributions to projection predictive inference, and present a safe, efficient, and modular workflow for prediction-oriented model selection therein. We also provide an interpretation of the projected posteriors achieved by projection predictive inference in terms of their limitations in causal settings.

翻译：在过去的十年中，贝叶斯预测、模型比较与模型选择的概念取得了显著发展。因此，贝叶斯学界见证了在构建和选择预测模型方面的理论与应用贡献的快速增长。投影预测推断在这方面尤其显示出前景，已在众多领域中得到应用。与单纯基于交叉验证或信息准则性能指标的朴素选择方法相比，它更不易过拟合，并且在预测性能方面已知优于其他方法。本文综述了投影预测推断的核心概念与当代贡献，并提出了一个面向预测的模型选择流程，该流程具有安全、高效和模块化的特点。我们还从因果设定中的局限性角度，对投影预测推断所获得的投影后验分布进行了解释。