Introduction to explainable machine learning using Stata

Aramayis Dallakyan
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Aramayis Dallakyan: StataCorp

UK Stata Conference 2026 from Stata Users Group

Abstract: Machine learning (ML) has become a powerful tool for modeling complex data and providing accurate predictions. However, the “black-box” nature of many ML models often raises concerns about their explainability and trustworthiness. Explainable machine learning (XML) seeks to address these concerns by enhancing the transparency and understanding of ML predictions. This talk aims to provide a practical guide to XML techniques. It begins with an overview of ensemble decision tree models such as random forests and gradient boosting, which are widely used but often difficult to interpret. I then introduce methods for explaining predictions using both global and local XML techniques. These include state-of-the-art approaches such as SHAP values, individual conditional expectation (ICE) plots, variable importance measures, partial dependence plots, and global surrogate models.

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Persistent link: https://EconPapers.repec.org/RePEc:boc:lsug26:11

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