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A Theory of Experimenters

Author

Listed:
  • Abhijit Banerjee
  • Sylvain Chassang
  • Sergio Montero
  • Erik Snowberg

Abstract

This paper proposes a decision-theoretic framework for experiment design. We model experimenters as ambiguity-averse decision-makers, who make trade-offs between subjective expected performance and robustness. This framework accounts for experimenters' preference for randomization, and clarifies the circumstances in which randomization is optimal: when the available sample size is large enough or robustness is an important concern. We illustrate the practical value of such a framework by studying the issue of rerandomization. Rerandomization creates a trade-off between subjective performance and robustness. However, robustness loss grows very slowly with the number of times one randomizes. This argues for rerandomizing in most environments.

Suggested Citation

  • Abhijit Banerjee & Sylvain Chassang & Sergio Montero & Erik Snowberg, 2017. "A Theory of Experimenters," NBER Working Papers 23867, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:23867
    Note: DEV ED HC HE LS PE POL
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    File URL: http://www.nber.org/papers/w23867.pdf
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    References listed on IDEAS

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    1. Federico A. Bugni & Ivan A. Canay & Azeem M. Shaikh, 2015. "Inference under covariate-adaptive randomization," CeMMAP working papers CWP45/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Humphreys, Macartan & Sanchez de la Sierra, Raul & van der Windt, Peter, 2013. "Fishing, Commitment, and Communication: A Proposal for Comprehensive Nonbinding Research Registration," Political Analysis, Cambridge University Press, vol. 21(1), pages 1-20, January.
    3. Toru Kitagawa & Aleksey Tetenov, 2018. "Who Should Be Treated? Empirical Welfare Maximization Methods for Treatment Choice," Econometrica, Econometric Society, vol. 86(2), pages 591-616, March.
    4. Tetenov, Aleksey, 2012. "Statistical treatment choice based on asymmetric minimax regret criteria," Journal of Econometrics, Elsevier, vol. 166(1), pages 157-165.
    5. Kasy, Maximilian, 2016. "Why Experimenters Might Not Always Want to Randomize, and What They Could Do Instead," Political Analysis, Cambridge University Press, vol. 24(3), pages 324-338, July.
    6. Edward Miguel & Michael Kremer, 2004. "Worms: Identifying Impacts on Education and Health in the Presence of Treatment Externalities," Econometrica, Econometric Society, vol. 72(1), pages 159-217, January.
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    Citations

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    Cited by:

    1. Davide Viviano, 2020. "Experimental Design under Network Interference," Papers 2003.08421, arXiv.org.
    2. Pedro Carneiro & Sokbae (Simon) Lee & Daniel Wilhelm, 2016. "Optimal data collection for randomized control trials," CeMMAP working papers CWP15/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. Luc Behaghel & Karen Macours, & Julie Subervie, 2018. "Can RCTs help improve the design of CAP," Working Papers hal-01974425, HAL.
    4. Luc Behaghel & Karen Macours & Julie Subervie, 2019. "How can randomised controlled trials help improve the design of the common agricultural policy?," European Review of Agricultural Economics, Foundation for the European Review of Agricultural Economics, vol. 46(3), pages 473-493.
    5. Deaton, Angus & Cartwright, Nancy, 2018. "Understanding and misunderstanding randomized controlled trials," Social Science & Medicine, Elsevier, vol. 210(C), pages 2-21.

    More about this item

    JEL classification:

    • C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

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