Stratification Trees for Adaptive Randomization in Randomized Controlled Trials
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This paper proposes a two-stage adaptive randomization procedure for randomized controlled trials. The method uses data from a first-stage pilot experiment to determine how to stratify in a second wave of the experiment, where the objective is to minimize the variance of an estimator for the average treatment effect (ATE). We consider selection from a class of stratified randomization procedures which we call stratification trees: these are procedures whose strata can be represented as decision trees, with differing treatment assignment probabilities across strata. By using the pilot to estimate a stratification tree, we simultaneously select which covariates to use for stratification, how to stratify over these covariates, as well as the assignment probabilities within these strata. Our main result shows that using this randomization procedure with an appropriate estimator results in an asymptotic variance which minimizes the variance bound for estimating the ATE, over an optimal stratification of the covariate space. Moreover, by extending techniques developed in Bugni et al. (2018), the results we present are able to accommodate a large class of assignment mechanisms within strata, including stratified block randomization. We also present extensions of the procedure to the setting of multiple treatments, and to the targeting of subgroup-specific effects. In a simulation study, we find that our method is most effective when the response model exhibits some amount of "sparsity" with respect to the covariates, but can be effective in other contexts as well, as long as the pilot sample size used to estimate the stratification tree is not prohibitively small. We conclude by applying our method to the study in Karlan and Wood (2017), where we estimate a stratification tree using the first wave of their experiment.
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