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Choosing Treatment Policies Under Ambiguity

Author

Listed:
  • Charles F. Manski

    (Department of Economics and Institute for Policy Research, Northwestern University, Evanston, Illinois 60208)

Abstract

Economists studying choice with partial knowledge typically assume that the decision maker places a subjective distribution on unknown quantities and maximizes expected utility. Someone lacking a subjective distribution faces a problem of choice under ambiguity. This article reviews recent research on policy choice under ambiguity, when the task is to choose treatments for a population. Ambiguity arises when a planner has partial knowledge of treatment response and does not feel able to place a subjective distribution on the unknowns. I first discuss dominance and alternative criteria for choice among undominated policies. I then illustrate with the choice of a vaccination policy by a planner who has partial knowledge of the effect of vaccination on illness. I next study a class of problems in which a planner may want to cope with ambiguity by diversification, assigning observationally identical persons to different treatments. Lastly, I consider a setting in which a planner should not diversify treatment.

Suggested Citation

  • Charles F. Manski, 2011. "Choosing Treatment Policies Under Ambiguity," Annual Review of Economics, Annual Reviews, vol. 3(1), pages 25-49, September.
    • Handle: RePEc:anr:reveco:v:3:y:2011:p:25-49
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    File URL: http://www.annualreviews.org/doi/abs/10.1146/annurev-economics-061109-080359
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    Citations

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    Cited by:

    1. Charles F. Manski, 2018. "Survey Measurement of Probabilistic Macroeconomic Expectations: Progress and Promise," NBER Macroeconomics Annual, University of Chicago Press, vol. 32(1), pages 411-471.
    2. Berger, Loïc & Bleichrodt, Han & Eeckhoudt, Louis, 2013. "Treatment decisions under ambiguity," Journal of Health Economics, Elsevier, vol. 32(3), pages 559-569.
    3. Charles F. Manski, 2014. "Choosing Size of Government Under Ambiguity: Infrastructure Spending and Income Taxation," Economic Journal, Royal Economic Society, vol. 0(576), pages 359-376, May.
    4. Stanca, Lorenzo, 2021. "Smooth aggregation of Bayesian experts," Journal of Economic Theory, Elsevier, vol. 196(C).
    5. Hahn, Jinyong & Ridder, Geert & Snider, Connan, 2013. "Partial identification and mergers," Economics Letters, Elsevier, vol. 118(1), pages 126-129.
    6. Makoto Abe, 2024. "Probability-based A/B testing with Adaptive Minimax Regret (AMR) Criterion for Long-Term Customer Metrics," CIRJE F-Series CIRJE-F-1234, CIRJE, Faculty of Economics, University of Tokyo.
    7. Riccardo D'Adamo, 2021. "Orthogonal Policy Learning Under Ambiguity," Papers 2111.10904, arXiv.org, revised Dec 2022.
    8. Lihua Lei & Roshni Sahoo & Stefan Wager, 2023. "Policy Learning under Biased Sample Selection," Papers 2304.11735, arXiv.org.
    9. Dionissi Aliprantis, 2017. "Assessing the evidence on neighborhood effects from Moving to Opportunity," Empirical Economics, Springer, vol. 52(3), pages 925-954, May.
    10. Besley, Tim & Mueller, Hannes, 2015. "Volatility and Political Institutions: Theory and Application to Economic Growth," CEPR Discussion Papers 10374, C.E.P.R. Discussion Papers.
    11. Nabil I. Al-Najjar & Luciano Pomatto, 2016. "Choice under aggregate uncertainty," Theory and Decision, Springer, vol. 80(2), pages 187-209, February.
    12. repec:bos:wpaper:wp2013-001 is not listed on IDEAS
    13. Epstein, Larry G. & Seo, Kyoungwon, 2015. "Exchangeable capacities, parameters and incomplete theories," Journal of Economic Theory, Elsevier, vol. 157(C), pages 879-917.
    14. Cosmin L. Ilut & Martin Schneider, 2022. "Modeling Uncertainty as Ambiguity: a Review," NBER Working Papers 29915, National Bureau of Economic Research, Inc.
    15. Manski, Charles F., 2013. "Public Policy in an Uncertain World: Analysis and Decisions," Economics Books, Harvard University Press, number 9780674066892, Spring.
    16. Charles F. Manski, 2014. "Vaccine Approvals and Mandates Under Uncertainty: Some Simple Analytics," NBER Working Papers 20432, National Bureau of Economic Research, Inc.

    More about this item

    Keywords

    dominance; minimax regret; partial identification; planning; social choice; treatment response;
    All these keywords.

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • H42 - Public Economics - - Publicly Provided Goods - - - Publicly Provided Private Goods

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