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.
(2017), Observation and Experiment: An Introduction to Causal Infe rence, Harvard University Press
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
stat.ME 1years
2020 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.