Machine Learning Methods that Economists Should Know About

Posted: 4 Sep 2019

See all articles by Susan Athey

Susan Athey

Stanford University

Guido W. Imbens

Stanford Graduate School of Business

Date Written: August 2019

Abstract

We discuss the relevance of the recent machine learning (ML) literature for economics and econometrics. First we discuss the differences in goals, methods, and settings between the ML literature and the traditional econometrics and statistics literatures. Then we discuss some specific methods from the ML literature that we view as important for empirical researchers in economics. These include supervised learning methods for regression and classification, unsupervised learning methods, and matrix completion methods. Finally, we highlight newly developed methods at the intersection of ML and econometrics that typically perform better than either off-the-shelf ML or more traditional econometric methods when applied to particular classes of problems, including causal inference for average treatment effects, optimal policy estimation, and estimation of the counterfactual effect of price changes in consumer choice models.

Suggested Citation

Athey, Susan and Imbens, Guido W., Machine Learning Methods that Economists Should Know About (August 2019). Annual Review of Economics, Vol. 11, pp. 685-725, 2019. Available at SSRN: https://ssrn.com/abstract=3445877 or http://dx.doi.org/10.1146/annurev-economics-080217-053433

Susan Athey (Contact Author)

Stanford University ( email )

Stanford, CA 94305
United States

Guido W. Imbens

Stanford Graduate School of Business ( email )

655 Knight Way
Stanford, CA 94305-5015
United States

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