Charles F. Manski, Aleksey Tetenov
arXiv 9 Aug 2023 · Econometrics · publishedJapanese Economic Review (2023) · 7 citations (OpenAlex)
arXiv:2308.05171 · PDF · DOI · OpenAlex
The statistical decision theory pioneered by Wald (1950) has used state-dependent mean loss (risk) to measure the performance of statistical decision functions across potential samples. We think it evident that evaluation of performance should respect stochastic dominance, but we do not see a compelling reason to focus exclusively on mean loss. We think it instructive to also measure performance by other functionals that respect stochastic dominance, such as quantiles of the distribution of loss. This paper develops general principles and illustrative applications for statistical decision theory respecting stochastic dominance. We modify the Wald definition of admissibility to an analogous concept of stochastic dominance (SD) admissibility, which uses stochastic dominance rather than mean sampling performance to compare alternative decision rules. We study SD admissibility in two relatively simple classes of decision problems that arise in treatment choice. We reevaluate the relationship between the MLE, James-Stein, and James-Stein positive part estimators from the perspective of SD admissibility. We consider alternative criteria for choice among SD-admissible rules. We juxtapose traditional criteria based on risk, regret, or Bayes risk with analogous ones based on quantiles of state-dependent sampling distributions or the Bayes distribution of loss.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Regret Analysis in Threshold Policy Design | 0.644 | 2 | 2 |
| 2 | Treatment Choice with Nonlinear Regret | 0.585 | 3 | 1 |
| 3 | Policy Learning with Distributional Welfare | 0.405 | 1 | 1 |
| 4 | Robust Bayes Treatment Choice with Partial Identification | 0.405 | 1 | 1 |
| 5 | Leave No One Undermined: Policy Targeting with Regret Aversion | 0.405 | 1 | 1 |
| 6 | Wasserstein Policy Learning for Distributional Outcomes | 0.405 | 1 | 1 |