The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
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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Cites
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
Human Decisions and Machine Predictions
Kleinberg, J., Lakkaraju, H., Leskovec, J., Ludwig, J., & Mullainathan, S. (2018) · The Quarterly Journal of Economics · doi:10.1093/qje/qjx032 · Cited by 1,040
Self-citationCan You Recognize an Effective Teacher When You Recruit One?
Rockoff, J. E., Jacob, B. A., Kane, T. J., & Staiger, D. O. (2011) · Education Finance and Policy · doi:10.1162/edfp_a_00022 · Cited by 390
Agent-based modelling of consumer energy choices
Rai, V., & Henry, A. D. (2016) · Nature Climate Change · doi:10.1038/nclimate2967 · Cited by 252
Productivity and Selection of Human Capital with Machine Learning
Chalfin, A. J., Danieli, O., Hillis, A., Jelveh, Z., Luca, M., Ludwig, J., & Mullainathan, S. (2016) · American Economic Review · doi:10.1257/aer.p20161029 · Cited by 190
Self-citationHamel, M. B. (2008) · Archives of Internal Medicine · doi:10.1001/archinte.168.13.1430 · Cited by 177
Where Not to Eat? Improving Public Policy by Predicting Hygiene Inspections Using Online Reviews
Kang, J. R. W., Kuznetsova, P., Luca, M., & Choi, Y. (2013) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d13-1150 · Cited by 89
Predicting and Preventing Shootings among At-Risk Youth
Chandler, D. R., Levitt, S. D., & List, J. A. (2011) · American Economic Review · doi:10.1257/aer.101.3.288 · Cited by 60
Where Not to Eat? Improving Public Policy by Predicting Hygiene Inspections Using Online ReviewsPreprint
Kang, J. R. W., Kuznetsova, P., Choi, Y., & Luca, M. (2013) · SSRN Electronic Journal · doi:10.2139/ssrn.2293165 · Cited by 42
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Dissecting racial bias in an algorithm used to manage the health of populations
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019) · Science · doi:10.1126/science.aax2342 · Cited by 7,137
Self-citationPredicting the future - big data, machine learning, and clinical medicine
Obermeyer, Z., & Emanuel, E. J. (2016) · New England Journal of Medicine · doi:10.1056/nejmp1606181 · Cited by 3,652
Self-citationEstimation and Inference of Heterogeneous Treatment Effects using Random Forests
Wager, S., & Athey, S. (2018) · Journal of the American Statistical Association · doi:10.1080/01621459.2017.1319839 · Cited by 2,917
Machine Learning: An Applied Econometric Approach
Mullainathan, S., & Spiess, J. (2017) · Journal of Economic Perspectives · doi:10.1257/jep.31.2.87 · Cited by 1,989
Self-citationThe State of Applied Econometrics: Causality and Policy Evaluation
Athey, S. C., & Imbens, G. W. (2017) · The Journal of Economic Perspectives · doi:10.1257/jep.31.2.3 · Cited by 1,406
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
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