Optimal data collection for randomized control trials

Pedro Carneiro (), Sokbae Lee () and Daniel Wilhelm
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Pedro Carneiro: Institute for Fiscal Studies and University College London
Sokbae Lee: Institute for Fiscal Studies and Institute for Fiscal Studies

No CWP15/17, CeMMAP working papers from Centre for Microdata Methods and Practice, 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 modi?cation 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.

Keywords: randomized control trials; big data; data collection; optimal survey design; orthogonal greedy algorithm; survey costs. (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-dev, nep-exp and nep-pay
Date: 2017-03-27
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Related works:
Working Paper: Optimal data collection for randomized control trials (2016)
Working Paper: Optimal Data Collection for Randomized Control Trials (2016)
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