Large deviation asymptotics for statistical treatment rules
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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- Karl Schlag, 2006. "ELEVEN - Tests needed for a Recommendation," Economics Working Papers ECO2006/2, European University Institute.
- 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.
- Keisuke Hirano & Jack R. Porter, 2009.
"Asymptotics for Statistical Treatment Rules,"
Econometric Society, vol. 77(5), pages 1683-1701, 09.
- Charles F. Manski, 2003.
"Statistical treatment rules for heterogeneous populations,"
CeMMAP working papers
CWP03/03, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Charles F. Manski, 2004. "Statistical Treatment Rules for Heterogeneous Populations," Econometrica, Econometric Society, vol. 72(4), pages 1221-1246, 07.
- 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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