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Data and Code for: Optimality of Matched-Pair Designs in Randomized Controlled Trials
Principal Investigator(s): View help for Principal Investigator(s) Yuehao Bai, University of Michigan
Version: View help for Version V1
| Name | File Type | Size | Last Modified |
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| code | 06/10/2022 01:10:PM | ||
| data | 06/10/2022 01:10:PM | ||
| results | 09/29/2022 10:31:PM | ||
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text/plain | 382 bytes | 09/29/2022 04:46:PM |
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application/pdf | 118.9 KB | 09/30/2022 06:24:AM |
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text/plain | 3.2 KB | 09/29/2022 06:30:PM |
Project Citation:
Bai, Yuehao. Data and Code for: Optimality of Matched-Pair Designs in Randomized Controlled Trials. Nashville, TN: American Economic Association [publisher], 2022. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2022-11-21. https://doi.org/10.3886/E171321V1
Project Description
Summary:
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In randomized controlled trials (RCTs), treatment is often assigned by stratified randomization. I show that among all stratified randomization schemes which treat all units with probability one half, a certain matched-pair design achieves the maximum statistical precision for estimating the average treatment effect (ATE). In an important special case, the optimal design pairs units according to the baseline outcome. In a simulation study based on datasets from 10 RCTs, this design lowers the standard error for the estimator of the ATE by 10% on average, and by up to 34%, relative to the original designs.
Scope of Project
JEL Classification:
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C12 Hypothesis Testing: General
C13 Estimation: General
C14 Semiparametric and Nonparametric Methods: General
C90 Design of Experiments: General
C12 Hypothesis Testing: General
C13 Estimation: General
C14 Semiparametric and Nonparametric Methods: General
C90 Design of Experiments: General
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