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.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: IZA Discussion Papers ; No. 9908

Klassifikation und Themen

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

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Carneiro, Pedro ; Lee, Sokbae ; Wilhelm, Daniel
• Ereignis: Veröffentlichung
• (wer): Institute for the Study of Labor (IZA)
• (wo): Bonn
• (wann): 2016

Weitere Informationen

• Handle: http://hdl.handle.net/10419/142347
• Letzte Aktualisierung: 20.09.2024, 08:23 MESZ