IDEAS home Printed from https://ideas.repec.org/a/ecm/emetrp/v72y2004i4p1221-1246.html
   My bibliography  Save this article

Statistical Treatment Rules for Heterogeneous Populations

Author

Listed:
  • Charles F. Manski

Abstract

An important objective of empirical research on treatment response is to provide decision makers with information useful in choosing treatments. This paper studies minimax-regret treatment choice using the sample data generated by a classical randomized experiment. Consider a utilitarian social planner who must choose among the feasible statistical treatment rules, these being functions that map the sample data and observed covariates of population members into a treatment allocation. If the planner knew the population distribution of treatment response, the optimal treatment rule would maximize mean welfare conditional on all observed covariates. The appropriate use of covariate information is a more subtle matter when only sample data on treatment response are available. I consider the class of conditional empirical success rules; that is, rules assigning persons to treatments that yield the best experimental outcomes conditional on alternative subsets of the observed covariates. I derive a closed-form bound on the maximum regret of any such rule. Comparison of the bounds for rules that condition on smaller and larger subsets of the covariates yields sufficient sample sizes for productive use of covariate information. When the available sample size exceeds the sufficiency boundary, a planner can be certain that conditioning treatment choice on more covariates is preferable (in terms of minimax regret) to conditioning on fewer covariates. Copyright The Econometric Society 2004.

Suggested Citation

  • Charles F. Manski, 2004. "Statistical Treatment Rules for Heterogeneous Populations," Econometrica, Econometric Society, vol. 72(4), pages 1221-1246, July.
  • Handle: RePEc:ecm:emetrp:v:72:y:2004:i:4:p:1221-1246
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/j.1468-0262.2004.00530.x
    File Function: link to full text
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    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. Manski, Charles F., 2000. "Identification problems and decisions under ambiguity: Empirical analysis of treatment response and normative analysis of treatment choice," Journal of Econometrics, Elsevier, vol. 95(2), pages 415-442, April.
    2. Frank Stafford, 1985. "Income-Maintenance Policy and Work Effort: Learning from Experiments and Labor-Market Studies," NBER Chapters, in: Social Experimentation, pages 95-144, National Bureau of Economic Research, Inc.
    3. Dehejia, Rajeev H., 2005. "Program evaluation as a decision problem," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 141-173.
    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. Charles F. Manski, 1999. "Statistical Treatment Rules for Heterogeneous Populations: With Application to Randomized Experiments," NBER Technical Working Papers 0242, National Bureau of Economic Research, Inc.
    2. Guido W. Imbens & Jeffrey M. Wooldridge, 2009. "Recent Developments in the Econometrics of Program Evaluation," Journal of Economic Literature, American Economic Association, vol. 47(1), pages 5-86, March.
    3. Romain Aeberhardt & Élise Coudin & Roland Rathelot, 2017. "The heterogeneity of ethnic employment gaps," Journal of Population Economics, Springer;European Society for Population Economics, vol. 30(1), pages 307-337, January.
    4. Keisuke Hirano & Jack R. Porter, 2016. "Panel Asymptotics and Statistical Decision Theory," The Japanese Economic Review, Japanese Economic Association, vol. 67(1), pages 33-49, March.
    5. Stefanie Behncke & Markus Frölich & Michael Lechner, 2009. "Targeting Labour Market Programmes - Results from a Randomized Experiment," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 145(III), pages 221-268, September.
    6. Charles F. Manski, 2005. "Fractional Treatment Rules for Social Diversification of Indivisible Private Risks," NBER Working Papers 11675, National Bureau of Economic Research, Inc.
    7. Bas van der Klaauw & Sandra Vriend, 2015. "A Nonparametric Method for Predicting Survival Probabilities," Tinbergen Institute Discussion Papers 15-126/V, Tinbergen Institute.
    8. Abhijit V. Banerjee & Esther Duflo, 2009. "The Experimental Approach to Development Economics," Annual Review of Economics, Annual Reviews, vol. 1(1), pages 151-178, May.
    9. Timothy Christensen & Hyungsik Roger Moon & Frank Schorfheide, 2020. "Robust Forecasting," Papers 2011.03153, arXiv.org, revised Dec 2020.
    10. Basu, Anirban, 2015. "Welfare implications of learning through solicitation versus diversification in health care," Journal of Health Economics, Elsevier, vol. 42(C), pages 165-173.
    11. Stoye, Jörg, 2012. "Minimax regret treatment choice with covariates or with limited validity of experiments," Journal of Econometrics, Elsevier, vol. 166(1), pages 138-156.
    12. Juliano Assunção & Robert McMillan & Joshua Murphy & Eduardo Souza-Rodrigues, 2019. "Optimal Environmental Targeting in the Amazon Rainforest," NBER Working Papers 25636, National Bureau of Economic Research, Inc.
    13. Dehejia, Rajeev H., 2005. "Program evaluation as a decision problem," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 141-173.
    14. William A. Brock & Steven N. Durlauf & Kenneth D. West, 2003. "Policy Evaluation in Uncertain Economic Environments," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 34(1), pages 235-322.
    15. Charles F. Manski, 2000. "Using Studies of Treatment Response to Inform Treatment Choice in Heterogeneous Populations," NBER Technical Working Papers 0263, National Bureau of Economic Research, Inc.
    16. Tuomas Takalo & Tanja Tanayama & Otto Toivanen, 2005. "Selection Or Self-Rejection? Applications Into A Treatment," Industrial Organization 0510002, University Library of Munich, Germany.
    17. Kohei Yata, 2021. "Optimal Decision Rules Under Partial Identification," Papers 2111.04926, arXiv.org.
    18. Mark C. Berger & Dan Black & Jeffrey Smith, 2000. "Evaluating Profiling as a Means of Allocating Government Services," University of Western Ontario, Departmental Research Report Series 200018, University of Western Ontario, Department of Economics.
    19. Toru Kitagawa & Sokbae Lee & Chen Qiu, 2022. "Treatment Choice with Nonlinear Regret," Papers 2205.08586, arXiv.org, revised Jul 2022.
    20. Oscar Mitnik, 2008. "How do Training Programs Assign Participants to Training? Characterizing the Assignment Rules of Government Agencies for Welfare-to-Work Programs in California," Working Papers 0907, University of Miami, Department of Economics.

    More about this item

    Statistics

    Access and download statistics

    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:ecm:emetrp:v:72:y:2004:i:4:p:1221-1246. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: https://edirc.repec.org/data/essssea.html .

    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: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/essssea.html .

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.