Machine learning (ML), data analytics, and elements of artificial intelligence (AI) have dominated headlines in recent decades, initially with an engineering focus on numerical efficiency and regularisation techniques. One may have called this the era of establishing the "mechanics of ML". While dimension-flexible and nonparametric methods, such as random forests, have become standard in many scientific applications, economics is now poised for deeper integration with these ML approaches.

This Special Issue aims to showcase research that strengthens the growing link between ML methodologies and economic analysis, demonstrating how computational innovations can address fundamental economic questions. By bringing together researchers working at this intersection, we seek to establish an avenue of methodological pathways and create methodological frameworks that harness the predictive power of modern ML and AI while preserving the structural understanding essential to economic research.

We particularly welcome contributions in risk analysis, causal inference, predictive modelling, and policy evaluation that employ ML and large-language-model setups while maintaining economic interpretability.