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Inference on Optimal Treatment Assignments

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    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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    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
    Contact details of provider: Postal: Yale University, Box 208281, New Haven, CT 06520-8281 USA
    Phone: (203) 432-3702
    Fax: (203) 432-6167
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    Order Information: Postal: Cowles Foundation, Yale University, Box 208281, New Haven, CT 06520-8281 USA

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    1. Rajeev Dehejia, 1999. "Program Evaluation as a Decision Problem," NBER Working Papers 6954, National Bureau of Economic Research, Inc.
    2. Hahn, Jinyong & Hirano, Keisuke & Karlan, Dean, 2011. "Adaptive Experimental Design Using the Propensity Score," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(1), pages 96-108.
    3. Doug Miller & A. Colin Cameron & Jonah B. Gelbach, 2006. "Bootstrap-Based Improvements for Inference with Clustered Errors," Working Papers 621, University of California, Davis, Department of Economics.
    4. Aleksey Tetenov, 2009. "Statistical Treatment Choice Based on Asymmetric Minimax Regret Criteria," Carlo Alberto Notebooks 119, Collegio Carlo Alberto.
    5. 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.
    6. Debopam Bhattacharya & Pascaline Dupas, 2008. "Inferring Welfare Maximizing Treatment Assignment under Budget Constraints," NBER Working Papers 14447, National Bureau of Economic Research, Inc.
    7. Joseph P. Romano & Michael Wolf, 2005. "Exact and Approximate Stepdown Methods for Multiple Hypothesis Testing," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 94-108, March.
    8. repec:idb:brikps:77778 is not listed on IDEAS
    9. Charles Manski, 2003. "Statistical treatment rules for heterogeneous populations," CeMMAP working papers CWP03/03, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    10. Tristen Hayfield & Jeffrey S. Racine, . "Nonparametric Econometrics: The np Package," Journal of Statistical Software, American Statistical Association, vol. 27(i05).
    11. 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, vol. 103(484), pages 1481-1495.
    12. Victor Chernozhukov & Han Hong & Elie Tamer, 2007. "Estimation and Confidence Regions for Parameter Sets in Econometric Models," Econometrica, Econometric Society, vol. 75(5), pages 1243-1284, 09.
    13. 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.
    14. Kasy, Maximilian, 2013. "Why experimenters should not randomize, and what they should do instead," Working Paper 36154, Harvard University OpenScholar.
    15. Joseph P. Romano & Azeem M. Shaikh, 2010. "Inference for the Identified Set in Partially Identified Econometric Models," Econometrica, Econometric Society, vol. 78(1), pages 169-211, 01.
    16. Stoye, Jörg, 2009. "Minimax regret treatment choice with finite samples," Journal of Econometrics, Elsevier, vol. 151(1), pages 70-81, July.
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