# Predictive economics: Rethinking economic methodology with machine learning

arXiv: https://arxiv.org/abs/2510.04726 · HTML: https://mlsanity.com/paper/2510.04726/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 20 references that are in the map.
- First posted on arXiv 6 Oct 2025.

## Builds on (most cited first, 3 of 3)

- [Machine Learning Methods Economists Should Know About](https://mlsanity.com/paper/1903.10075/) (2019)
- [How is Machine Learning Useful for Macroeconomic Forecasting?](https://mlsanity.com/paper/2008.12477/) (2020)
- [Explaining Exchange Rate Forecasts with Macroeconomic Fundamentals Using Interpretive Machine Learning](https://mlsanity.com/paper/2303.16149/) (2023)

## Abstract (arXiv)

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.