Infusing economically motivated structure into machine learning methods

Marcus Buckmann and Galina Potjagailo
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Marcus Buckmann: Bank of England
Galina Potjagailo: Bank of England

No 1144, Bank of England Staff Working Paper series from Bank of England

Abstract: This paper discusses how economic theory can be integrated into machine learning (ML) models to enhance their interpretability and applicability for policy analysis. While ML methods offer considerable flexibility and strong predictive performance, they are often criticised for their 'black box' nature and lack of economic transparency. A growing body of research addresses this limitation by introducing structure into ML models − most notably through Block-Additive Models (BAMs) and theory-consistent monotonicity constraints. BAMs group predictors into economically meaningful blocks and impose additivity across blocks, while permitting non-linearities and interactions within them. This architecture enables clear attribution of each block’s contribution to the model’s predictions. Monotonicity constraints further improve interpretability by aligning the model’s directional responses with economic theory, allowing for the separation of opposing effects − such as distinguishing between supply- and demand-driven components of inflation. Empirical evidence shows that these structured ML approaches retain strong predictive performance while yielding economically meaningful narratives.

Keywords: Interpretable machine learning; theory-aligned constraints; macroeconomic analysis (search for similar items in EconPapers)
JEL-codes: C10 C14 C53 (search for similar items in EconPapers)
Pages: 28
Date: 2025-09-26
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Persistent link: https://EconPapers.repec.org/RePEc:boe:boeewp:023266

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