IDEAS home Printed from https://ideas.repec.org/a/spr/jecrev/v74y2023i4d10.1007_s42973-023-00147-0.html
   My bibliography  Save this article

Minimax-regret treatment rules with many treatments

Author

Listed:
  • Matthew A. Masten

    (Duke University)

Abstract

Statistical treatment rules map data into treatment choices. Optimal treatment rules maximize social welfare. Although some finite sample results exist, it is generally difficult to prove that a particular treatment rule is optimal. This paper develops asymptotic and numerical results on minimax-regret treatment rules when there are many treatments. I first extend a result of Hirano and Porter (Econometrica 77:1683–1701, 2009) to show that an empirical success rule is asymptotically optimal under the minimax-regret criterion. The key difference is that I use a permutation invariance argument from Lehmann (Ann Math Stat 37:1–6, 1966) to solve the limit experiment instead of applying results from hypothesis testing. I then compare the finite sample performance of several treatment rules. I find that the empirical success rule performs poorly in unbalanced designs, and that when prior information about treatments is symmetric, balanced designs are preferred to unbalanced designs. Finally, I discuss how to compute optimal finite sample rules by applying methods from computational game theory.

Suggested Citation

  • Matthew A. Masten, 2023. "Minimax-regret treatment rules with many treatments," The Japanese Economic Review, Springer, vol. 74(4), pages 501-537, October.
    • Handle: RePEc:spr:jecrev:v:74:y:2023:i:4:d:10.1007_s42973-023-00147-0
    DOI: 10.1007/s42973-023-00147-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s42973-023-00147-0
    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/s42973-023-00147-0?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 search for a different version of it.

    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_s42973-023-00147-0. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.