Ding, Peng; Feller, Avi; Miratrix, Luke Randomization inference for treatment effect variation. (English) Zbl 1414.62146 J. R. Stat. Soc., Ser. B, Stat. Methodol. 78, No. 3, 655-671 (2016). Summary: Applied 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. Cited in 14 Documents MSC: 62G10 Nonparametric hypothesis testing 62A01 Foundations and philosophical topics in statistics 62P10 Applications of statistics to biology and medical sciences; meta analysis Keywords:causal inference; head start; heterogeneous treatment effect; randomization test PDFBibTeX XMLCite \textit{P. Ding} et al., J. R. Stat. Soc., Ser. B, Stat. Methodol. 78, No. 3, 655--671 (2016; Zbl 1414.62146) Full Text: DOI arXiv