Optimal Data Collection for Randomized Control Trials

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: IZA Discussion Papers ; No. 9908

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 ; Lee, Sokbae ; Wilhelm, Daniel
• Event: Veröffentlichung
• (who): Institute for the Study of Labor (IZA)
• (where): Bonn
• (when): 2016

Further information

• Handle: http://hdl.handle.net/10419/142347
• Last update: 10.03.2025, 11:41 AM CET