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Minimax-Regret Sample Selection in Randomized Experiments

Yuchen Hu, Henry Zhu, Emma Brunskill, Stefan Wager

arXiv 3 Mar 2024 · Statistics — Methodology · 2 citations (OpenAlex)

arXiv:2403.01386 · PDF · DOI · OpenAlex · Extracted main text

Abstract

Randomized controlled trials are often run in settings with many subpopulations that may have differential benefits from the treatment being evaluated. We consider the problem of sample selection, i.e., whom to enroll in a randomized trial, such as to optimize welfare in a heterogeneous population. We formalize this problem within the minimax-regret framework, and derive optimal sample-selection schemes under a variety of conditions. Using data from a COVID-19 vaccine trial, we also highlight how different objectives and decision rules can lead to meaningfully different guidance regarding optimal sample allocation.

Citation extraction

46
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appendix boundary found by appendix_command · 64% of the source is main text. Read the extracted text to check this.

Most heavily cited references

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.

ReferenceIntensityMentionsSectionsMain text
1Gaganpreet Sharma (2017) Pros and cons of different sampling techniques0.84333100%
2Lindsey R. Baden, Hana M. El Sahly, Brandon Essink, Karen Kotloff, S… (2021) Efficacy and Safety of the mRNA-1273 SARS-CoV-2 Vaccine0.69391100%
3Hervé Moulin (2004) Fair division and collective welfare0.64422100%
4Ajay S. Singh and Micah B. Masuku (2013) Fundamentals of Applied Research and Sampling Techniques0.64422100%
5Charles F. Manski and Aleksey Tetenov (2016) Sufficient trial size to inform clinical practice0.58531100%
6Charles F. Manski and Aleksey Tetenov (2019) Trial Size for Near-Optimal Choice Between Surveillance and Aggressive Treatment: Reconsidering MSLT-II0.58531100%
7Andrew Gelman, John B Carlin, Hal S Stern, David B Dunson, Aki Vehta… (2013) Bayesian Data Analysis (3rd ed.)0.5112250%
8Aleksey Tetenov (2012) Statistical treatment choice based on asymmetric minimax regret criteria0.5112250%
9Eduardo M. Azevedo, Alex Deng, José Luis Montiel Olea, Justin Rao, a… (2020) A/B Testing with Fat Tails0.51121100%
10Eduardo M. Azevedo, David Mao, José Luis Montiel Olea, and Amilcar V… (2023) The A/B testing problem with Gaussian priors0.51121100%

Showing the top 10 of 46 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Externally Valid Selection of Experimental Sites via the k-Median Problem0.40511
2Selecting the Best Arm in One-Shot Multi-Arm RCTs: The Asymptotic Minimax-Regret Decision Framework for the Best-Population Selection Problem0.40511
3Learning What to Learn: Experimental Design when Combining Experimental with Observational Evidence0.40511
4Prior-Free Sample Size Design for Test-and-Roll Experiments0.40511
5Policy Learning with Observational Data : The Case of Hepatitis C Treatment for HIV/HCV Co-Infected Patients0.40511
62.5cm When and How to Pilot: Design Rules for Two-Wave Experiments0.40511