Randomization Inference with Sample Attrition (opens in new tab)

arXiv:2507.00795v2 Announce Type: replace-cross Abstract: 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 ...