  Rerandomization discards assignments with covariates unbalanced in the
treatment and control groups to improve the estimation and inference
efficiency. However, the acceptance-rejection sampling method used by
rerandomization is computationally inefficient. As a result, it is
time-consuming for classical rerandomization to draw numerous independent
assignments, which are necessary for constructing Fisher randomization tests.
To address this problem, we propose a pair-switching rerandomization method to
draw balanced assignments much efficiently. We show that the
difference-in-means estimator is unbiased for the average treatment effect and
the Fisher randomization tests are valid under pair-switching rerandomization.
In addition, our method is applicable in both non-sequentially and sequentially
randomized experiments. We conduct comprehensive simulation studies to compare
the finite-sample performances of the proposed method and classical
rerandomization. Simulation results indicate that pair-switching
rerandomization leads to comparable power of Fisher randomization tests and is
4-18 times faster than classical rerandomization. Finally, we apply the
pair-switching rerandomization method to analyze two clinical trial data sets,
both demonstrating the advantages of our method.
