Randomization Inference for Small Samples: Power and Size Control

Abstract

Randomization inference (RI) is often recommended for small-sample experiments, yet standard RI tests can be severely underpowered relative to parametric alternatives. We derive power bounds for common RI procedures and show that matching on pre-treatment covariates recovers most of the power loss at no cost to size control, even under sharp-null violation.