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 Southern California

Version:  View help for Version V2

Name File Type Size Last Modified
  code 06/10/2022 01:10:PM
  data 06/10/2022 01:10:PM
  results 09/29/2022 10:31:PM
CHANGES.txt text/plain 270 bytes 05/20/2024 12:26:PM
Dockerfile text/plain 382 bytes 09/29/2022 04:46:PM
README.pdf application/pdf 118.9 KB 09/30/2022 06:24:AM
run 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], 2024. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2024-05-21. https://doi.org/10.3886/E171321V2

Project Description

Summary:  View help for Summary
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:  View help for JEL Classification
      C12 Hypothesis Testing: General
      C13 Estimation: General
      C14 Semiparametric and Nonparametric Methods: General
      C90 Design of Experiments: General


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Published Versions

V2 [2024-05-21]

V1 [2022-11-21]

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