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Large deviation asymptotics for statistical treatment rules

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  • Otsu, Taisuke

Abstract

This note applies large deviation-based optimality theory to evaluate treatment rules for treatment assignment problems. We find nearly optimal treatment rules whose asymptotic maximum large deviation risks can be arbitrary close to the corresponding minimax bounds.

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File URL: http://www.sciencedirect.com/science/article/B6V84-4S9G916-1/2/9c51b7e5874fd8fbbf76bc555ac188ea
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Bibliographic Info

Article provided by Elsevier in its journal Economics Letters.

Volume (Year): 101 (2008)
Issue (Month): 1 (October)
Pages: 53-56

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Handle: RePEc:eee:ecolet:v:101:y:2008:i:1:p:53-56

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Web page: http://www.elsevier.com/locate/ecolet

Related research

Keywords: Treatment rule Large deviation;

References

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  1. Stoye, Jörg, 2009. "Minimax regret treatment choice with finite samples," Journal of Econometrics, Elsevier, vol. 151(1), pages 70-81, July.
  2. Hirano, Keisuke & Porter, Jack, 2006. "Asymptotics for statistical treatment rules," MPRA Paper 1173, University Library of Munich, Germany.
  3. Karl Schlag, 2006. "ELEVEN - Tests needed for a Recommendation," Economics Working Papers ECO2006/2, European University Institute.
  4. Charles Manski, 2003. "Statistical treatment rules for heterogeneous populations," CeMMAP working papers CWP03/03, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  5. 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.
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