用机器学习提升经济预测精度，突破传统因果分析局限。
Predictive economics: Rethinking economic methodology with machine learning
- 以预测准确率为核心，融合机器学习方法重构经济研究范式。
- 在复杂数据场景中，预测模型显著提升实证分析效果。
- 适合关注模型泛化能力与实际应用的研究者参考。
本文提出预测经济学作为一种独立的经济分析视角，基于机器学习，以预测准确性为核心目标，而非因果识别。借鉴弗里德曼的工具主义传统、舒穆利的解释-预测区分以及布雷曼的建模文化对比，将预测确立为合法的认识论与方法论目标。通过回顾各经济子领域的最新应用，表明预测模型在复杂或数据丰富的背景下能有效支持实证分析。该视角补充了现有方法，推动更包容的方法论——既重视模型的样本外表现，也兼顾可解释性与理论结构。
原文摘要 · Abstract (English)
This article proposes predictive economics as a distinct analytical perspective within economics, grounded in machine learning and centred on predictive accuracy rather than causal identification. Drawing on the instrumentalist tradition (Friedman), the explanation-prediction divide (Shmueli), and the contrast between modelling cultures (Breiman), we formalise prediction as a valid epistemological and methodological objective. Reviewing recent applications across economic subfields, we show how predictive models contribute to empirical analysis, particularly in complex or data-rich contexts. This perspective complements existing approaches and supports a more pluralistic methodology - one that values out-of-sample performance alongside interpretability and theoretical structure.
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