Asymptotic theory of rerandomization in treatment–control experiments
Abstract
Rerandomization refers to experimental designs that enforce covariate balance. This paper studies the asymptotic properties of the difference-in-means estimator under rerandomization, based on the randomness of the treatment assignment without imposing any parametric modeling assumptions on the covariates or outcome. The non-Gaussian asymptotic distribution allows for constructing large-sample confidence intervals for the average treatment effect and demonstrates the advantages of rerandomization over complete randomization.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Aug 27, 2018
- Source ID
- 10.1073/pnas.1808191115
Entities
People
- Donald B. Rubin
- Peng Ding
- Xinran Li
Organizations
- Harvard University
- Institute of Education Sciences
- National Institute of Allergy and Infectious Diseases
- National Science Foundation
- Office of Naval Research
- Statistics New Zealand