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Fractional Treatment Rules for Social Diversification of Indivisible Private Risks

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  • Charles F. Manski

Abstract

Should a social planner treat observationally identical persons identically? This paper shows that uniform treatment is not necessarily desirable when a planner has only partial knowledge of treatment response. Then there may be reason to implement a fractional treatment rule, with positive fractions of the observationally identical persons receiving different treatments. The planning problems studied here share some important features: treatment is individualistic, social welfare is a strictly increasing function of a population mean outcome, and outcomes depend on an unknown state of nature. They differ in the information that the planner has about the state of nature and in how he uses this information to make treatment choices. In particular, I compare treatment choice using Bayes rules and the minimax-regret criterion. Following the analysis, I put aside the literal notion of a planner who makes decisions on behalf of society and consider the feasibility of implementing fractional treatment rules in functioning democracies.

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Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 11675.

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Date of creation: Oct 2005
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Handle: RePEc:nbr:nberwo:11675

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  1. Hirano, Keisuke & Porter, Jack, 2006. "Asymptotics for statistical treatment rules," MPRA Paper 1173, University Library of Munich, Germany.
  2. Sheshinski, E. & Diamond, P., 1992. "Economic Aspects of Optimal Disability Benefits," Working papers 92-5, Massachusetts Institute of Technology (MIT), Department of Economics.
  3. Rajeev Dehejia, 1999. "Program Evaluation as a Decision Problem," NBER Working Papers 6954, National Bureau of Economic Research, Inc.
  4. Steven Shavell & A. Mitchell Polinsky, 2000. "The Economic Theory of Public Enforcement of Law," Journal of Economic Literature, American Economic Association, vol. 38(1), pages 45-76, March.
  5. Manski, Charles F., 2007. "Minimax-regret treatment choice with missing outcome data," Journal of Econometrics, Elsevier, vol. 139(1), pages 105-115, July.
  6. 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.
  7. Yi Cheng, 2003. "Choosing sample size for a clinical trial using decision analysis," Biometrika, Biometrika Trust, vol. 90(4), pages 923-936, December.
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