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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. To design the experiment, a researcher needs to solve this tradeoff subject to her budget constraint. We show that this optimization problem is equivalent to optimally predicting outcomes by the covariates, which in turn can be solved using existing machine learning techniques using pre-experimental data such as other similar studies, a census, or a household survey. 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.
Authors
Research Fellow Columbia University
Sokbae is an IFS Research Fellow and a Professor at Columbia University, with an interest in Econometrics, Applied Microeconomics and Statistics.
Research Fellow University College London
Pedro is a Professor of Economics at University College London and an economist in the IFS' Centre for Microdata Methods and Practice (cemmap).
Research Associate LMU Munich
Daniel is a Research Associate of the IFS in Cemmap and Professor of Statistics and Econometrics at LMU Munich.
Working Paper details
- DOI
- 10.1920/wp.cem.2019.2119
- Publisher
- The IFS
Suggested citation
P, Carneiro and S, Lee and D, Wilhelm. (2019). Optimal Data Collection for Randomized Control Trials. London: The IFS. Available at: https://ifs.org.uk/publications/optimal-data-collection-randomized-control-trials-2 (accessed: 4 November 2024).
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