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.
“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
Section IV, “Prediction Problems Are Common and Important” (page unavailable), reports that machine learning can improve on judges’ predictions, supporting the accuracy part of the claim. The passages do not establish that algorithms make policy-relevant predictions more fairly or consistently than public servants. (LLM, conf=0.88)
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Enhancing the adoption of digital public services: Evidence from a large-scale field experiment
Hyytinen, A., Tuimala, J., & Hammar, M. (2022) · Government Information Quarterly · doi:10.1016/j.giq.2022.101687 · Cited by 61 · see citing works
- 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 › Decision Sciences › Management Science and Operations Research › 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
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