Machine Learning Methods Economists Should Know About

TL;DR

This paper reviews key machine learning methods relevant for economics, highlighting their differences from traditional approaches and discussing recent advances that improve empirical analysis in economic research.

Contribution

It identifies important ML techniques for economists and discusses new hybrid methods that outperform traditional and off-the-shelf ML approaches in specific economic problems.

Findings

Supervised learning methods are crucial for regression and classification tasks.

Unsupervised learning methods provide valuable insights into economic data.

New hybrid ML-econometrics methods enhance causal inference and policy analysis.

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 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.