A Conditional Randomization Test to Account for Covariate Imbalance in Randomized Experiments

Hennessy Jonathan, Dasgupta Tirthankar (), Miratrix Luke, Pattanayak Cassandra and Sarkar Pradipta
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Hennessy Jonathan: Department of Statistics, Harvard University, Cambridge, MA, USA
Dasgupta Tirthankar: Department of Statistics, Harvard University, Cambridge, MA, USA
Miratrix Luke: Department of Statistics, Harvard University, Cambridge, MA, USA
Pattanayak Cassandra: Quantitative Analysis Institute, Wellesley College, Wellesley, MA, USA
Sarkar Pradipta: Principal Scientist, Procter & Gamble International Operations, Singapore

Journal of Causal Inference, 2016, vol. 4, issue 1, 61-80

Abstract: We 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 adjustment 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.

Keywords: sharp null hypothesis; potential outcomes; balance function (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:bpj:causin:v:4:y:2016:i:1:p:61-80:n:3

DOI: 10.1515/jci-2015-0018

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