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Inference on Optimal Treatment Assignments

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    Abstract

    We consider inference on optimal treatment assignments. Our methods allow for inference on the treatment assignment rule that would be optimal given knowledge of the population treatment effect in a general setting. The procedure uses multiple hypothesis testing methods to determine a subset of the population for which assignment to treatment can be determined to be optimal after conditioning on all available information, with a prespecified level of confidence. A monte carlo study confirms that the inference procedure has good small sample behavior. We apply the method to study the Mexican conditional cash transfer program Progresa.

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    File URL: http://cowles.econ.yale.edu/P/cd/d19a/d1927-r.pdf
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    Bibliographic Info

    Paper provided by Cowles Foundation for Research in Economics, Yale University in its series Cowles Foundation Discussion Papers with number 1927R.

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    Length: 32 pages
    Date of creation: Nov 2013
    Date of revision: Apr 2014
    Handle: RePEc:cwl:cwldpp:1927r

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    Keywords: Treatment assignment; Multiple testing;

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    1. repec:idb:brikps:77778 is not listed on IDEAS
    2. A. Colin Cameron & Jonah B. Gelbach & Douglas L. Miller, 2007. "Bootstrap-Based Improvements for Inference with Clustered Errors," NBER Technical Working Papers 0344, National Bureau of Economic Research, Inc.
    3. Hahn, Jinyong & Hirano, Keisuke & Karlan, Dean, 2009. "Adaptive Experimental Design Using the Propensity Score," Working Papers 59, Yale University, Department of Economics.
    4. Charles F. Manski, 2004. "Statistical Treatment Rules for Heterogeneous Populations," Econometrica, Econometric Society, Econometric Society, vol. 72(4), pages 1221-1246, 07.
    5. Joseph Hotz, V. & Imbens, Guido W. & Mortimer, Julie H., 2005. "Predicting the efficacy of future training programs using past experiences at other locations," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 241-270.
    6. Aleksey Tetenov, 2009. "Statistical Treatment Choice Based on Asymmetric Minimax Regret Criteria," Carlo Alberto Notebooks 119, Collegio Carlo Alberto.
    7. Kasy, Maximilian, 2013. "Why experimenters should not randomize, and what they should do instead," Working Paper 36154, Harvard University OpenScholar.
    8. Dehejia, Rajeev H., 2005. "Program evaluation as a decision problem," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 141-173.
    9. Joseph Romano & Michael Wolf, 2003. "Exact and approximate stepdown methods for multiple hypothesis testing," Economics Working Papers 727, Department of Economics and Business, Universitat Pompeu Fabra.
    10. Paul Schultz, T., 2004. "School subsidies for the poor: evaluating the Mexican Progresa poverty program," Journal of Development Economics, Elsevier, vol. 74(1), pages 199-250, June.
    11. Victor Chernozhukov & Han Hong & Elie Tamer, 2007. "Estimation and Confidence Regions for Parameter Sets in Econometric Models," Econometrica, Econometric Society, Econometric Society, vol. 75(5), pages 1243-1284, 09.
    12. Anderson, Michael L., 2008. "Multiple Inference and Gender Differences in the Effects of Early Intervention: A Reevaluation of the Abecedarian, Perry Preschool, and Early Training Projects," Journal of the American Statistical Association, American Statistical Association, American Statistical Association, vol. 103(484), pages 1481-1495.
    13. Bhattacharya, Debopam & Dupas, Pascaline, 2012. "Inferring welfare maximizing treatment assignment under budget constraints," Journal of Econometrics, Elsevier, vol. 167(1), pages 168-196.
    14. Stoye, Jörg, 2009. "Minimax regret treatment choice with finite samples," Journal of Econometrics, Elsevier, vol. 151(1), pages 70-81, July.
    15. Tristen Hayfield & Jeffrey S. Racine, . "Nonparametric Econometrics: The np Package," Journal of Statistical Software, American Statistical Association, American Statistical Association, vol. 27(i05).
    16. Joseph P. Romano & Azeem M. Shaikh, 2010. "Inference for the Identified Set in Partially Identified Econometric Models," Econometrica, Econometric Society, Econometric Society, vol. 78(1), pages 169-211, 01.
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