arXiv 3 Aug 2022 · Statistics — Methodology · 1 citations (OpenAlex)
arXiv:2208.02038 · PDF · DOI · OpenAlex · Extracted main text
Decision-making often involves ranking and selection. For example, to assemble a team of political forecasters, we might begin by narrowing our choice set to the candidates we are confident rank among the top 10% in forecasting ability. Unfortunately, we do not know each candidate's true ability but observe a noisy estimate of it. This paper develops new Bayesian algorithms to rank and select candidates based on noisy estimates. Using simulations based on empirical data, we show that our algorithms often outperform frequentist ranking and selection algorithms. Our Bayesian ranking algorithms yield shorter rank confidence intervals while maintaining approximately correct coverage. Our Bayesian selection algorithms select more candidates while maintaining correct error rates. We apply our ranking and selection procedures to field experiments, economic mobility, forecasting, and similar problems. Finally, we implement our ranking and selection techniques in a user-friendly Python package documented here: https://dsbowen-conditional-inference.readthedocs.io/en/latest/.
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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 | Magne Mogstad, Joseph P Romano, Azeem Shaikh, and Daniel Wilhelm (2020) Inference for ranks with applications to mobility across neighborhoods and academic achievement across countries | 1.000 | 5 | 3 | 100% |
| 2 | Jiaying Gu and Roger Koenker (2020) Invidious comparisons: Ranking and selection as compound decisions | 0.811 | 4 | 2 | 100% |
| 3 | Magne Mogstad, Joseph Romano, Azeem Shaikh, and Daniel Wilhelm (2022) Comment on “invidious comparisons: Ranking and selection as compound decisions” | 0.737 | 3 | 2 | 100% |
| 4 | Katherine L Milkman, Dena Gromet, Hung Ho, Joseph S Kay, Timothy W L… (2021) Megastudies improve the impact of applied behavioural science | 0.644 | 2 | 2 | 100% |
| 5 | Andreas Schleicher (2019) Pisa 2018: Insights and interpretations | 0.644 | 2 | 2 | 100% |
| 6 | Philip E Tetlock and Dan Gardner (2016) Superforecasting: The art and science of prediction | 0.644 | 2 | 2 | 100% |
| 7 | Dillon Bowen (2022) Multiple inference: A python package for comparing multiple parameters self | 0.511 | 2 | 1 | 100% |
| 8 | Junhui Cai, Xu Han, Ya'acov Ritov, and Linda Zhao (2021) Nonparametric empirical bayes estimation and testing for sparse and heteroscedastic signals | 0.511 | 2 | 1 | 100% |
| 9 | James O Berger (2013) Statistical decision theory and Bayesian analysis | 0.405 | 1 | 1 | 100% |
| 10 | Peter Bergman, Raj Chetty, Stefanie DeLuca, Nathaniel Hendren, Lawre… (2019) Creating moves to opportunity: Experimental evidence on barriers to neighborhood choice | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 28 scored citations.