# Minimax-Regret Sample Selection in Randomized Experiments

arXiv: https://arxiv.org/abs/2403.01386 · HTML: https://mlsanity.com/paper/2403.01386/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Mar 2024.

## Built on by (most cited first, 3 of 3)

- [Policy Learning with Observational Data: The Case of Hepatitis C Treatment for HIV/HCV Co-Infected Patients](https://mlsanity.com/paper/2605.16593/) (2026)
- [Experimentation and Commitment under Reward Shifts](https://mlsanity.com/paper/2607.23432/) (2026)
- [Target-Weighted Neyman Allocation: Experimental Design for Heterogeneous Treatment Effects under Population Shift](https://mlsanity.com/paper/2608.06512/) (2026)

## Abstract (arXiv)

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.