Randomization Inference with Sample Attrition
Presented by
Zeyang (Arthur) Yu, PhD
Princeton University
Thursday, May 28, 2026, 12:00pm – 1:30pm over Zoom
Randomization inference is a widely-used and appealing approach for analyzing treatment effects in randomized experiments, as it is finite-sample valid and does not require any distributional assumptions. However, naive application of randomization inference may suffer from severe size distortion in the presence of sample attrition, where outcome data are missing for some units. In this paper, we propose new, computationally efficient methods for randomization inference that remain valid under a broad class of potentially informative missingness mechanisms, allowing a unit’s missingness to depend on its (unobserved) potential outcomes.
Flyer and Registration