IDEAS home Printed from https://ideas.repec.org/a/spr/jecrev/v74y2023i4d10.1007_s42973-023-00144-3.html
   My bibliography  Save this article

Treatment choice, mean square regret and partial identification

Author

Listed:
  • Toru Kitagawa

    (Brown University
    University College London)

  • Sokbae Lee

    (Columbia University)

  • Chen Qiu

    (Cornell University)

Abstract

We consider a decision maker who faces a binary treatment choice when their welfare is only partially identified from data. We contribute to the literature by anchoring our finite-sample analysis on mean square regret, a decision criterion advocated by Kitagawa et al. in (2022) "Treatment Choice with Nonlinear Regret" . We find that optimal rules are always fractional, irrespective of the width of the identified set and precision of its estimate. The optimal treatment fraction is a simple logistic transformation of the commonly used t-statistic multiplied by a factor calculated by a simple constrained optimization. This treatment fraction gets closer to 0.5 as the width of the identified set becomes wider, implying the decision maker becomes more cautious against the adversarial Nature.

Suggested Citation

  • Toru Kitagawa & Sokbae Lee & Chen Qiu, 2023. "Treatment choice, mean square regret and partial identification," The Japanese Economic Review, Springer, vol. 74(4), pages 573-602, October.
    • Handle: RePEc:spr:jecrev:v:74:y:2023:i:4:d:10.1007_s42973-023-00144-3
    DOI: 10.1007/s42973-023-00144-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s42973-023-00144-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s42973-023-00144-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Eric Mbakop & Max Tabord‐Meehan, 2021. "Model Selection for Treatment Choice: Penalized Welfare Maximization," Econometrica, Econometric Society, vol. 89(2), pages 825-848, March.
    2. Charles F. Manski, 1989. "Anatomy of the Selection Problem," Journal of Human Resources, University of Wisconsin Press, vol. 24(3), pages 343-360.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Vira Semenova, 2023. "Aggregated Intersection Bounds and Aggregated Minimax Values," Papers 2303.00982, arXiv.org, revised Jun 2024.
    2. Charles F. Manski, 2021. "Econometrics for Decision Making: Building Foundations Sketched by Haavelmo and Wald," Econometrica, Econometric Society, vol. 89(6), pages 2827-2853, November.
    3. Daido Kido, 2023. "Locally Asymptotically Minimax Statistical Treatment Rules Under Partial Identification," Papers 2311.08958, arXiv.org.
    4. Christian Bontemps & Thierry Magnac & Eric Maurin, 2012. "Set Identified Linear Models," Econometrica, Econometric Society, vol. 80(3), pages 1129-1155, May.
    5. Mullahy, John, 2024. "Analyzing health outcomes measured as bounded counts," Journal of Health Economics, Elsevier, vol. 95(C).
    6. Manski, Charles F., 2023. "Probabilistic prediction for binary treatment choice: With focus on personalized medicine," Journal of Econometrics, Elsevier, vol. 234(2), pages 647-663.
    7. Wiedner, Jonas & Giesecke, Johannes, 2022. "Immigrant Men’s Economic Adaptation in Changing Labor Markets: Why Gaps between Turkish and German Men Expanded, 1976–2015," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 56(1), pages 176-205.
    8. Breunig, Christoph & Mammen, Enno & Simoni, Anna, 2018. "Nonparametric estimation in case of endogenous selection," Journal of Econometrics, Elsevier, vol. 202(2), pages 268-285.
    9. Monique De Haan & Edwin Leuven, 2020. "Head Start and the Distribution of Long-Term Education and Labor Market Outcomes," Journal of Labor Economics, University of Chicago Press, vol. 38(3), pages 727-765.
    10. Ta-Wei Huang & Eva Ascarza, 2024. "Doing More with Less: Overcoming Ineffective Long-Term Targeting Using Short-Term Signals," Marketing Science, INFORMS, vol. 43(4), pages 863-884, July.
    11. E. Michael Foster & Leonard Bickman, 1996. "An Evaluator's Guide To Detecting Attrition Problems," Evaluation Review, , vol. 20(6), pages 695-723, December.
    12. Arie Beresteanu & Francesca Molinari, 2008. "Asymptotic Properties for a Class of Partially Identified Models," Econometrica, Econometric Society, vol. 76(4), pages 763-814, July.
    13. Dimitris Christelis & Dimitris Georgarakos & Tullio Jappelli & Geoff Kenny, 2020. "The Covid-19 Crisis and Consumption: Survey Evidence from Six EU Countries," Working Papers 2020_31, Business School - Economics, University of Glasgow.
    14. Greg Lewis & Vasilis Syrgkanis, 2018. "Adversarial Generalized Method of Moments," Papers 1803.07164, arXiv.org, revised Apr 2018.
    15. Charles Bellemare & Luc Bissonnette & Sabine Kröger, 2010. "Bounding preference parameters under different assumptions about beliefs: a partial identification approach," Experimental Economics, Springer;Economic Science Association, vol. 13(3), pages 334-345, September.
    16. Giorgio Brunello & Dimitris Christelis & Anna Sanz‐de‐Galdeano & Anastasia Terskaya, 2024. "Does college selectivity reduce obesity? A partial identification approach," Health Economics, John Wiley & Sons, Ltd., vol. 33(10), pages 2306-2320, October.
    17. Markus Frölich, 2004. "Programme Evaluation with Multiple Treatments," Journal of Economic Surveys, Wiley Blackwell, vol. 18(2), pages 181-224, April.
    18. Zhou Yu & Dowell Myers, 2010. "Misleading Comparisons of Homeownership Rates when the Variable Effect of Household Formation Is Ignored: Explaining Rising Homeownership and the Homeownership Gap between Blacks and Asians in the US," Urban Studies, Urban Studies Journal Limited, vol. 47(12), pages 2615-2640, November.
    19. Semenova, Vira, 2023. "Debiased machine learning of set-identified linear models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1725-1746.
    20. Manski, Charles F. & Molinari, Francesca, 2021. "Estimating the COVID-19 infection rate: Anatomy of an inference problem," Journal of Econometrics, Elsevier, vol. 220(1), pages 181-192.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:jecrev:v:74:y:2023:i:4:d:10.1007_s42973-023-00144-3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.