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Machine Learning Methods Economists Should Know About

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Document pages: 62 pages

Abstract: We discuss the relevance of the recent Machine Learning (ML) literature foreconomics and econometrics. First we discuss the differences in goals, methodsand settings between the ML literature and the traditional econometrics andstatistics literatures. Then we discuss some specific methods from the machinelearning literature that we view as important for empirical researchers ineconomics. These include supervised learning methods for regression andclassification, unsupervised learning methods, as well as matrix completionmethods. Finally, we highlight newly developed methods at the intersection ofML and econometrics, methods that typically perform better than eitheroff-the-shelf ML or more traditional econometric methods when applied toparticular classes of problems, problems that include causal inference foraverage treatment effects, optimal policy estimation, and estimation of thecounterfactual effect of price changes in consumer choice models.

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