articleAnnual Review of EconomicsJun 10, 2019Closed access

Machine Learning Methods That Economists Should Know About

National Bureau of Economic Research · Stanford University

Indexed incrossref

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…

Citation impact

1,004
total citations
FWCI
87.05
Percentile
100%
References
114
Citations per year

Authors

2

Topics & keywords

Keywords
  • Counterfactual thinking
  • Econometrics
  • Causal inference
  • Computer science
  • Inference
  • Machine learning
  • Relevance (law)
  • Estimation
No related works found for this paper.