A new partitioning criterion based on kernel density estimates of covariates achieves better balance and more accurate difference-in-mean estimators than complete randomization or rerandomization in controlled experiments.
Sur l’extension du th´ eor` eme limite du calcu l des probabilit´ es aux sommes de quantit´ es d´ ependantes,
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Covariate Balancing Based on Kernel Density Estimates for Controlled Experiments
A new partitioning criterion based on kernel density estimates of covariates achieves better balance and more accurate difference-in-mean estimators than complete randomization or rerandomization in controlled experiments.