Minimax-Regret Sample Selection in Randomized Experiments

TL;DR

This paper develops a minimax-regret framework for optimal sample selection in randomized experiments, accounting for heterogeneity in populations, and demonstrates its application using COVID-19 vaccine trial data.

Contribution

It introduces a novel minimax-regret approach for sample selection in heterogeneous populations, with derived optimal schemes and practical insights from real trial data.

Findings

Different objectives lead to different sample allocation strategies.

Optimal schemes depend on population heterogeneity and decision rules.

Application to COVID-19 vaccine data illustrates practical relevance.

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.