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Title: No star is good news: A unified look at rerandomization based onp-values from covariate balance tests Authors:  Anqi Zhao - National University of Singapore (Singapore) [presenting]
Peng Ding - University of California, Berkeley (United States)
Abstract: Randomized experiments balance all covariates on average and provide the gold standard for estimating treatment effects. Chance imbalances nevertheless exist more or less in realized treatment allocations, subjecting subsequent inference to possibly large variability and conditional bias. Modern social and biomedical scientic publications often require the reporting of covariate balance tables with not only covariate means by treatment group but also the associated p-values from signicance tests of their dierences. The practical need to avoid small p-values renders balance check and rerandomization by hypothesis testing standards an attractive tool for improving covariate balance in randomized experiments. Despite the intuitiveness of such practice and its possibly already widespread use in reality, the existing literature knows little about its implications on subsequent inference, subjecting many effectively rerandomized experiments to possibly inecient analyses. To fill this gap, we examine a variety of potentially useful schemes for rerandomization based on p-values (ReP) from covariate balance tests, and quantify their impact on subsequent inference. Specically, we focus on three estimators of the average treatment effect from the unadjusted, additive, and fully interacted linear regressions of the outcome on treatment, respectively, and derive their respective asymptotic sampling properties under ReP.