Ding, PengFeller, Avi IsaacMiratrix, Luke2017-03-032015Ding, Peng, Avi Feller, and Luke Miratrix. 2015. “Randomization Inference for Treatment Effect Variation.” Journal of the Royal Statistical Society: Series B (Statistical Methodology) 78 (3) (July 7): 655–671. Portico. doi:10.1111/rssb.12124.1369-7412http://nrs.harvard.edu/urn-3:HUL.InstRepos:30501584Applied researchers are increasingly interested in whether and how treatment effects vary in randomized evaluations, especially variation that is not explained by observed covariates. We propose a model-free approach for testing for the presence of such unexplained variation. To use this randomization-based approach, we must address the fact that the average treatment effect, which is generally the object of interest in randomized experiments, actually acts as a nuisance parameter in this setting. We explore potential solutions and advocate for a method that guarantees valid tests in finite samples despite this nuisance. We also show how this method readily extends to testing for heterogeneity beyond a given model, which can be useful for assessing the sufficiency of a given scientific theory. We finally apply our method to the National Head Start impact study, which is a large-scale randomized evaluation of a Federal preschool programme, finding that there is indeed significant unexplained treatment effect variation.en-USCausal inferenceRandomization testHead StartHeterogeneous treatment effectJournal Article2017-03-0310.1111/rssb.12124