Toru Kitagawa, Sokbae Lee, Chen Qiu
arXiv 17 May 2022 · Econometrics
arXiv:2205.08586 · PDF · Extracted main text
The literature focuses on the mean of welfare regret, which can lead to undesirable treatment choice due to sensitivity to sampling uncertainty. We propose to minimize the mean of a nonlinear transformation of regret and show that singleton rules are not essentially complete for nonlinear regret. Focusing on mean square regret, we derive closed-form fractions for finite-sample Bayes and minimax optimal rules. Our approach is grounded in decision theory and extends to limit experiments. The treatment fractions can be viewed as the strength of evidence favoring treatment. We apply our framework to a normal regression model and sample size calculation.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Hayashi, T (2008) Regret aversion and opportunity dependence | 1.000 | 13 | 4 | 100% |
| 2 | Manski, C. F (2004) Statistical treatment rules for heterogeneous populations | 1.000 | 9 | 3 | 100% |
| 3 | Manski, C. F (2021) a): Econometrics for decision making: Building foundations sketched by Haavelmo and Wald | 0.928 | 4 | 3 | 100% |
| 4 | Tetenov, A (2012) Statistical treatment choice based on asymmetric minimax regret criteria | 0.928 | 4 | 3 | 100% |
| 5 | Hirano, K. and J. R. Porter (2009) Asymptotics for statistical treatment rules | 0.843 | 10 | 4 | 60% |
| 6 | Savage, L (1951) The theory of statistical decision | 0.843 | 3 | 3 | 100% |
| 7 | Wald, A (1950) Statistical Decision Functions | 0.811 | 4 | 2 | 100% |
| 8 | Stoye, J (2009) Minimax regret treatment choice with finite samples | 0.811 | 4 | 2 | 100% |
| 9 | Hirano, K. and J. R. Porter (2020) Asymptotic analysis of statistical decision rules in econometrics, in | 0.737 | 3 | 2 | 100% |
| 10 | Lehmann, E. L. and G. Casella (1998) Theory of point estimation | 0.644 | 3 | 2 | 67% |
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arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.