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Optimal Data Collection for Randomized Control Trials

Carneiro, P; Lee, S; Wilhelm, D; (2019) Optimal Data Collection for Randomized Control Trials. (Cemmap CWP21/19). The Institute for Fiscal Studies: London, UK. Green open access

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Abstract

In a randomized control trial, the precision of an average treatment effect estimator 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 a census, or a household survey, to inform the choice of both the sample size and the covariates to be collected. Our procedure seeks to minimize the resulting average treatment effect estimator's mean squared error, 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 substantial gains of up to 58%, measured either in terms of reductions in data collection costs or in terms of improvements in the precision of the treatment effect estimator.

Type: Working / discussion paper
Title: Optimal Data Collection for Randomized Control Trials
Open access status: An open access version is available from UCL Discovery
DOI: 10.1920/wp.cem.2019.2119
Publisher version: https://doi.org/ 10.1920/wp.cem.2019.2119
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL SLASH
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS > Dept of Economics
URI: https://discovery.ucl.ac.uk/id/eprint/10030048
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