arXiv 13 Jun 2018 · Econometrics · publishedThe Review of Economic Studies (2022) · 26 citations (OpenAlex)
arXiv:1806.05127 · PDF · DOI · OpenAlex · Extracted main text
This paper proposes an adaptive randomization procedure for two-stage randomized controlled trials. The method uses data from a first-wave experiment in order 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 first wave 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 is minimal in the class of stratification trees. Moreover, the results we present are able to accommodate a large class of assignment mechanisms within strata, including stratified block randomization. In a simulation study, we find that our method, paired with an appropriate cross-validation procedure ,can improve on ad-hoc choices of stratification. We conclude by applying our method to the study in Karlan and Wood (2017), where we estimate stratification trees using the first wave of their experiment.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Karlan, Dean and Daniel H Wood (2017) The effect of effectiveness: Donor response to aid effectiveness in a direct mail fundraising experiment | 1.000 | 14 | 4 | 100% |
| 2 | Bai, Yuehao (2019) Optimality of matched-pair designs in randomized controlled trials | 0.874 | 6 | 2 | 100% |
| 3 | Bugni, Federico A and Mengsi Gao (2021) Inference under covariate-adaptive randomization with imperfect compliance | 0.843 | 3 | 3 | 100% |
| 4 | Bugni, Federico A, Ivan A Canay, and Azeem M Shaikh (2018) Inference under covariate adaptive randomization with multiple treatments. bugni2017 | 0.817 | 11 | 4 | 55% |
| 5 | Hahn, Jinyong, Keisuke Hirano, and Dean Karlan (2011) Adaptive experimental design using the propensity score | 0.811 | 5 | 2 | 80% |
| 6 | Antognini, Alessandro Baldi and Alessandra Giovagnoli (2004) A new Ôbiased coin designÕfor the sequential allocation of two treatments | 0.644 | 2 | 2 | 100% |
| 7 | Athey, Susan and Guido Imbens (2016) Recursive partitioning for heterogeneous causal effects | 0.644 | 2 | 2 | 100% |
| 8 | Efron, Bradley (1971) Forcing a sequential experiment to be balanced | 0.644 | 2 | 2 | 100% |
| 9 | Glennerster, Rachel and Kudzai Takavarasha (2013) Running randomized evaluations: A practical guide | 0.644 | 2 | 2 | 100% |
| 10 | Hahn, Jinyong (1998) On the role of the propensity score in efficient semiparametric estimation of average treatment effects | 0.644 | 2 | 2 | 100% |
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