Minimax regret treatment choice with finite samples
This paper applies the minimax regret criterion to choice between two treatments conditional on observation of a finite sample. The analysis is based on exact small sample regret and does not use asymptotic approximations or finite-sample bounds. Core results are: (i) Minimax regret treatment rules are well approximated by empirical success rules in many cases, but differ from them significantly-both in terms of how the rules look and in terms of maximal regret incurred-for small sample sizes and certain sample designs. (ii) Absent prior cross-covariate restrictions on treatment outcomes, they prescribe inference that is completely separate across covariates, leading to no-data rules as the support of a covariate grows. I conclude by offering an assessment of these results.
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- Brock,W.A., 2004.
"Profiling problems with partially identified structure,"
21, Wisconsin Madison - Social Systems.
- William A. Brock, 2006. "Profiling Problems With Partially Identified Structure," Economic Journal, Royal Economic Society, vol. 116(515), pages F427-F440, November.
- Charles 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.
- Tetenov, Aleksey, 2012.
"Statistical treatment choice based on asymmetric minimax regret criteria,"
Journal of Econometrics,
Elsevier, vol. 166(1), pages 157-165.
- Aleksey Tetenov, 2009. "Statistical Treatment Choice Based on Asymmetric Minimax Regret Criteria," Carlo Alberto Notebooks 119, Collegio Carlo Alberto.
- Stoye, Jörg, 2011. "Axioms for minimax regret choice correspondences," Journal of Economic Theory, Elsevier, vol. 146(6), pages 2226-2251.
- Stoye, J rg, 2007. "Minimax Regret Treatment Choice With Incomplete Data And Many Treatments," Econometric Theory, Cambridge University Press, vol. 23(01), pages 190-199, February.
- Jörg Stoye, 2011. "Statistical decisions under ambiguity," Theory and Decision, Springer, vol. 70(2), pages 129-148, February.
- Hirano, Keisuke & Porter, Jack, 2006.
"Asymptotics for statistical treatment rules,"
1173, University Library of Munich, Germany.
- Manski, Charles F., 2007. "Minimax-regret treatment choice with missing outcome data," Journal of Econometrics, Elsevier, vol. 139(1), pages 105-115, July.
- 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.
- Charles F. Manski, 2006.
"Search Profiling With Partial Knowledge of Deterrence,"
Royal Economic Society, vol. 116(515), pages F385-F401, November.
- Charles F. Manski, 2005. "Search Profiling with Partial Knowledge of Deterrence," NBER Working Papers 11848, National Bureau of Economic Research, Inc.
- Gilboa, Itzhak & Schmeidler, David, 1989. "Maxmin expected utility with non-unique prior," Journal of Mathematical Economics, Elsevier, vol. 18(2), pages 141-153, April.
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