A Conditional Randomization Test to Account for Covariate Imbalance in Randomized Experiments
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CitationHennessy, Jonathan, Tirthankar Dasgupta, Luke Miratrix, Cassandra Pattanayak, and Pradipta Sarkar. 2016. “A Conditional Randomization Test to Account for Covariate Imbalance in Randomized Experiments.” Journal of Causal Inference 0 (0) (January 3). doi:10.1515/jci-2015-0018.
AbstractWe consider the conditional randomization test as a way to account for covariate imbalance in randomized experiments. The test accounts for covariate imbalance by comparing the observed test statistic to the null distribution of the test statistic conditional on the observed covariate imbalance. We prove that the conditional randomization test has the correct significance level and introduce original notation to describe covariate balance more formally. Through simulation, we verify that conditional randomization tests behave like more traditional forms of covariate adjustmet but have the added benefit of having the correct conditional significance level. Finally, we apply the approach to a randomized product marketing experiment where covariate information was collected after randomization.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:30208852
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