{"ID":2859833,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.04726","arxiv_id":"2510.04726","title":"Predictive economics: Rethinking economic methodology with machine learning","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2510.04726","url_pdf":"https://arxiv.org/pdf/2510.04726v1","authors":"[\"Miguel Alves Pereira\"]","published":"2025-10-06T11:46:03Z","proceeding":"econ.GN","tasks":"[\"econ.GN\",\"cs.LG\"]","methods":"[]","has_code":false}