传统预测模型无法保证决策质量，本文揭示原因并提出新学习范式。
Decision-Focused Learning: When and Why Traditional Prediction Models Fail
- 直接用预测结果做优化会失效，需从决策目标反推学习方法
- 基于预测不确定性的数据收集策略在决策场景中可能适得其反
- 适合关注决策效果的运筹优化、供应链管理等实际应用者
将未知参数的预测结果直接用于下游优化问题，即“预测-然后-优化”范式，长期是不确定性下决策的标准方法。然而，预测精度提升并不必然带来决策质量改善。这一脱节促使运筹学界对决策聚焦学习（DFL）产生日益浓厚的兴趣。本文综述了DFL的最新进展，重点分析随机线性规划作为下游决策问题的情形。我们指出，传统统计学习中广泛使用的工具——如仅基于预测不确定性的数据收集策略，以及诸如Wasserstein距离之类的分布距离度量——在决策聚焦场景中并不适用，必须重新思考。文章总结了DFL区别于传统预测建模的关键特性，并为新型决策聚焦工具的发展提供洞见。
原文摘要 · Abstract (English)
Plugging predictions of unknown parameters into downstream optimization problems, often referred to as the ``predict-then-optimize'' paradigm, has long been a standard approach in decision-making under uncertainty. However, improved predictive accuracy does not, in general, translate into improved decision quality. This disconnect has motivated growing interest in decision-focused learning (DFL) within the operations research community. This tutorial reviews recent developments in DFL and highlights key methodological insights, with a particular focus on stochastic linear programming as the downstream decision-making problem. We discuss why several widely used tools in traditional statistical learning are not directly suited to decision-focused settings and must be rethought, including (i) data collection strategies driven purely by predictive uncertainty and (ii) distributional distance measures such as the Wasserstein distance. We summarize properties of DFL that distinguish it from conventional predictive modeling and provide insights into the development of new decision-focused tools.
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