Machine Learning Methods Economists Should Know About
It provides an overview for economists on integrating ML methods, but it is incremental as it synthesizes existing knowledge without introducing new techniques.
The paper discusses the relevance of machine learning methods for economics and econometrics, highlighting differences in goals and settings, and reviews specific ML techniques like supervised and unsupervised learning, as well as newly developed hybrid methods that outperform traditional approaches in areas such as causal inference and policy estimation.
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 machine learning literature that we view as important for empirical researchers in economics. These include supervised learning methods for regression and classification, unsupervised learning methods, as well as matrix completion methods. Finally, we highlight newly developed methods at the intersection of ML and econometrics, methods that typically perform better than either off-the-shelf ML or more traditional econometric methods when applied to particular classes of problems, problems that include causal inference for average treatment effects, optimal policy estimation, and estimation of the counterfactual effect of price changes in consumer choice models.