Optimal data collection for randomized control trials

Pedro Carneiro, Sokbae (Simon) Lee and Daniel Wilhelm

No 15/17, CeMMAP working papers from Institute for Fiscal Studies

Abstract: In a randomized control trial, the precision of an average treatment effect estimator and the power of the corresponding t-test can be improved either by collecting data on additional individuals, or by collecting additional covariates that predict the outcome variable. We propose the use of pre-experimental data such as other similar studies, a census, or a household survey, to inform the choice of both the sample size and the covariates to be collected. Our proce-dure seeks to minimize the resulting average treatment effect estimator’s mean squared error or the corresponding t-test’s power, subject to the researcher’s budget constraint. We rely on a modification of an orthogonal greedy algorithm that is conceptually simple and easy to implement in the presence of a large number of potential covariates, and does not require any tuning parameters. In two empirical applications, we show that our procedure can lead to reductions of up to 58% in the costs of data collection, or improvements of the same magnitude in the precision of the treatment effect estimator.

Date: 2017-03-27
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https://www.cemmap.ac.uk/wp-content/uploads/2020/08/CWP1517.pdf (application/pdf)

Related works:
Journal Article: Optimal data collection for randomized control trials (2020)
Working Paper: Optimal Data Collection for Randomized Control Trials (2019)
Working Paper: Optimal data collection for randomized control trials (2017)
Working Paper: Optimal data collection for randomized control trials (2017)
Working Paper: Optimal data collection for randomized control trials (2017)
Working Paper: Optimal Data Collection for Randomized Control Trials (2016)
Working Paper: Optimal data collection for randomized control trials (2016)
Working Paper: Optimal data collection for randomized control trials (2016)
Working Paper: Optimal Data Collection for Randomized Control Trials (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:azt:cemmap:15/17

DOI: 10.1920/wp.cem.2017.1517

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