Running Machine Learning in Stata: Performance and usability evaluation

This presentation provides a comprehensive survey reviewing machine learning (ML) commands in Stata. It will systematically categorize and summarize the available ML commands in Stata and evaluate their performance and usability for different tasks such as classification, regression, clustering, and dimension reduction. The presentation also provides examples of how to use these commands with real-world datasets and compare their performance. This review aims to help researchers and practitioners choose appropriate ML methods and related Stata tools for their specific research questions and datasets and to improve the efficiency and reproducibility of ML analyses using Stata. It concludes by discussing some limitations and future directions for ML research in Stata.

About the speaker

Giovanni Cerulli is researcher director at IRCrES-CNR, Research Institute on Sustainable Economic Growth, National Research Council of Italy, Unit of Rome. His research interest is in applied econometrics, with a special focus on causal inference, program evaluation, and machine learning applied to various fields of the social and epidemiological sciences. Giovanni has developed original causal inference models, such as dose-response and treatment models with social interaction providing Stata implementation. He has developed around twenty Stata commands for casual inference and machine learning working also on Stata/Python/R integration for this purpose. Giovanni is author of the book Econometric Evaluation of Socio-Economic Programs: Theory and Applications (Springer, 2015; second edition 2022), and of the forthcoming book Fundamentals of Supervised Machine Learning: with Applications in Python, R, and Stata (Springer). He has published his papers in several high-quality scientific journals, and is currently editor-in-chief of the International Journal of Computational Economics and Econometrics.

Links

• Giovanni's Website