Optimal data collection for randomized control trials

Arbeitspapier

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 procedure 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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: cemmap working paper ; No. CWP15/17

Classification and Topics

• Classification: Wirtschaft ; Large Data Sets: Modeling and Analysis ; Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
• Subject: randomized control trials ; big data ; data collection ; optimal survey design ; orthogonal greedy algorithm ; survey costs

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Carneiro, Pedro M. ; Lee, Sokbae ; Wilhelm, Daniel
• Event: Veröffentlichung
• (who): Centre for Microdata Methods and Practice (cemmap)
• (where): London
• (when): 2017

Further information

• DOI: doi:10.1920/wp.cem.2017.1517
• Handle: http://hdl.handle.net/10419/189710
• Last update: 10.03.2025, 11:42 AM CET