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Optimal Data Collection for Randomized Control Trials

Author

Listed:
  • Pedro Carneiro

    (Institute for Fiscal Studies and University College London)

  • Sokbae (Simon) Lee

    (Institute for Fiscal Studies and Columbia University)

  • Daniel Wilhelm

    (Institute for Fiscal Studies and University College London)

Abstract
In a randomized control trial, the precision of an average treatment e?ect 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 tradeo? 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 e?ect estimator.

Suggested Citation

  • Pedro Carneiro & Sokbae (Simon) Lee & Daniel Wilhelm, 2019. "Optimal Data Collection for Randomized Control Trials," CeMMAP working papers CWP21/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    • Handle: RePEc:ifs:cemmap:21/19
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    References listed on IDEAS

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    Cited by:

    1. Karthik Muralidharan & Mauricio Romero & Kaspar Wüthrich, 2019. "Factorial Designs, Model Selection, and (Incorrect) Inference in Randomized Experiments," NBER Working Papers 26562, National Bureau of Economic Research, Inc.
    2. Max Tabord-Meehan, 2023. "Stratification Trees for Adaptive Randomisation in Randomised Controlled Trials," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 90(5), pages 2646-2673.
    3. Eszter Czibor & David Jimenez‐Gomez & John A. List, 2019. "The Dozen Things Experimental Economists Should Do (More of)," Southern Economic Journal, John Wiley & Sons, vol. 86(2), pages 371-432, October.
    4. John A. List & Ian Muir & Gregory K. Sun, 2022. "Using Machine Learning for Efficient Flexible Regression Adjustment in Economic Experiments," NBER Working Papers 30756, National Bureau of Economic Research, Inc.
    5. Prakash, Shivendra & Markfort, Corey D., 2022. "A Monte-Carlo based 3-D ballistics model for guiding bat carcass surveys using environmental and turbine operational data," Ecological Modelling, Elsevier, vol. 470(C).
    6. Pons Rotger, Gabriel & Rosholm, Michael, 2020. "The Role of Beliefs in Long Sickness Absence: Experimental Evidence from a Psychological Intervention," IZA Discussion Papers 13582, Institute of Labor Economics (IZA).
    7. Aufenanger, Tobias, 2018. "Treatment allocation for linear models," FAU Discussion Papers in Economics 14/2017, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics, revised 2018.

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    More about this item

    JEL classification:

    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access

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