Machine Learning Methods That Economists Should Know About
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.
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Machine-learning algorithms are different from traditional statistical modeling as there is no formalization or a priori theorization of relationships between variables.
“Machine-learning algorithms, then, are different from traditional statistical modeling as there is no formalization or a priori theorization of relationships between variables (Athey and Imbens 2019).”
From Legitimacy of Algorithmic Decision-Making: Six Threats and the Need for a Calibrated Institutional Response (2022) · cites this paper as Athey and Imbens 2019
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- Authors
- Athey, S., & Imbens, G. W.
- Affiliations
- National Bureau of Economic Research (US)
- Venue
- Annual Review of Economics
- Publisher
- Annual Reviews
- Published
- 2019-08-02
- Volume
- 11
- Issue
- 1
- Pages
- 685-725
- ISSN
- 1941-1383 (ISSN-L)
- 1941-1391
- OpenAlex
- W2922705140
- Cited by
- 1,163
- References
- 111
- Citations by year
- 2026: 295 · 2025: 198 · 2024: 173 · 2023: 155 · 2022: 111 · 2021: 126 · 2020: 77 · 2019: 25 · 2018: 3
- FWCI
- 13.01
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Annual Review of Economics (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Keywords
- machine learning, econometrics, supervised learning, unsupervised learning, matrix completion, causal inference, average treatment effect, consumer choice models, price effects, regression analysis, classification
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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