  We present an optimized rerandomization design procedure for a non-sequential
treatment-control experiment. Randomized experiments are the gold standard for
finding causal effects in nature. But sometimes random assignments result in
unequal partitions of the treatment and control group visibly seen as imbalance
in observed covariates. There can additionally be imbalance on unobserved
covariates. Imbalance in either observed or unobserved covariates increases
treatment effect estimator error inflating the width of confidence regions and
reducing experimental power. "Rerandomization" is a strategy that omits poor
imbalance assignments by limiting imbalance in the observed covariates to a
prespecified threshold. However, limiting this threshold too much can increase
the risk of contracting error from unobserved covariates. We introduce a
criterion that combines observed imbalance while factoring in the risk of
inadvertently imbalancing unobserved covariates. We then use this criterion to
locate the optimal rerandomization threshold based on the practitioner's level
of desired insurance against high estimator error. We demonstrate the gains of
our designs in simulation and in a dataset from a large randomized experiment
in education. We provide an open source R package available on CRAN named
OptimalRerandExpDesigns which generates designs according to our algorithm.
