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Prediction Policy Problems
Open accessAbstract
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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“Evidence suggests that algorithms make policy-relevant predictions more accurately, fairly, and consistently than public servants ( Kleinberg et al., 2018 ;Kleinberg et al., 2015 ).”
From The flaws of policies requiring human oversight of government algorithms (2022) · cites this paper as Kleinberg et al., 2015
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Metadata
- 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)
- Venue
- American Economic Review
- Publisher
- American Economic Association
- Published
- 2015-05-01
- Volume
- 105
- Issue
- 5
- Pages
- 491-495
- ISSN
- 0002-8282 (ISSN-L)
- 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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