Cited by 603Advanced Causal Inference TechniquesEvaluation and Performance AssessmentMachine Learning in Healthcare

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Prediction Policy Problems

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Kleinberg, Jon, Ludwig, Jens, Mullainathan, Sendhil, Obermeyer, Ziad·2015·American Economic Review

DOI 10.1257/aer.p20151023PMC PMC4869349

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Abstract

Most empirical policy work focuses on causal inference. We argue an important class of policy problems does not require causal inference but instead requires predictive inference. Solving these “prediction policy problems” requires more than simple regression techniques, since these are tuned to generating unbiased estimates of coefficients rather than minimizing prediction error. We argue that new developments in the field of “machine learning” are particularly useful for addressing these prediction problems. We use an example from health policy to illustrate the large potential social welfare gains from improved prediction.

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Authors: Kleinberg, J., Ludwig, J., Mullainathan, S., & Obermeyer, Z.
Affiliations: Cornell University (US)
University of Chicago (US)
Harvard University (US)
Brigham and Women's Hospital (US)
ORCID: Kleinberg, Jon
Ludwig, Jens
Mullainathan, Sendhil
Obermeyer, Ziad
Venue: American Economic Review
Publisher: American Economic Association
Published: 2015-05-01
Volume: 105
Issue: 5
Pages: 491-495
ISSN: 0002-8282 (ISSN-L)
DOI: 10.1257/aer.p20151023
PMC: PMC4869349
OpenAlex: W2203167467
Cited by: 584
References: 9
Citations by year: 2026: 40 · 2025: 52 · 2024: 65 · 2023: 65 · 2022: 61 · 2021: 74 · 2020: 90 · 2019: 55 · 2018: 37 · 2017: 28 · 2016: 13 · 2015: 3
FWCI: 24.03
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Locations: American Economic Review (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
RePEc: Research Papers in Economics (submittedVersion)
Funders: National Institutes of Health (DP5OD012161)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
Social Sciences › Public Administration › Evaluation and Performance Assessment
Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Keywords: predictive inference, causal inference, machine learning, prediction error, health policy

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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Prediction Policy Problems

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks