  Rerandomization discards assignments with covariates unbalanced in the
treatment and control groups to improve estimation and inference efficiency.
However, the acceptance-rejection sampling method used in rerandomization is
computationally inefficient. As a result, it is time-consuming for
rerandomization to draw numerous independent assignments, which are necessary
for performing Fisher randomization tests and constructing randomization-based
confidence intervals. To address this problem, we propose a pair-switching
rerandomization method to draw balanced assignments efficiently. We obtain the
unbiasedness and variance reduction of the difference-in-means estimator and
show that the Fisher randomization tests are valid under pair-switching
rerandomization. Moreover, we propose an exact approach to invert Fisher
randomization tests to confidence intervals, which is faster than the existing
methods. In addition, our method is applicable to both non-sequentially and
sequentially randomized experiments. We conduct comprehensive simulation
studies to compare the finite-sample performance of the proposed method with
that of classical rerandomization. Simulation results indicate that
pair-switching rerandomization leads to comparable power of Fisher
randomization tests and is 3--23 times faster than classical rerandomization.
Finally, we apply the pair-switching rerandomization method to analyze two
clinical trial datasets, both of which demonstrate the advantages of our
method.
