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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," CESifo Working Paper Series 6678, CESifo.
  • Handle: RePEc:ces:ceswps:_6678
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    References listed on IDEAS

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    Cited by:

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    2. Eliaz, Kfir & Spiegler, Ran, 2022. "On incentive-compatible estimators," Games and Economic Behavior, Elsevier, vol. 132(C), pages 204-220.
    3. Davide Viviano, 2020. "Experimental Design under Network Interference," Papers 2003.08421, arXiv.org, revised Jul 2022.
    4. Luc Behaghel & Karen Macours & Julie Subervie, 2018. "Can RCTs help improve the design of CAP," Working Papers hal-01974425, HAL.
    5. Pablo Balán & Augustin Bergeron & Gabriel Tourek & Jonathan L. Weigel, 2022. "Local Elites as State Capacity: How City Chiefs Use Local Information to Increase Tax Compliance in the Democratic Republic of the Congo," American Economic Review, American Economic Association, vol. 112(3), pages 762-797, March.
    6. 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, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 46(3), pages 473-493.
    7. Deaton, Angus & Cartwright, Nancy, 2018. "Understanding and misunderstanding randomized controlled trials," Social Science & Medicine, Elsevier, vol. 210(C), pages 2-21.
    8. Kfir Eliaz & Ran Spiegler, 2019. "The Model Selection Curse," American Economic Review: Insights, American Economic Association, vol. 1(2), pages 127-140, September.
    9. Pedro Carneiro & Sokbae (Simon) Lee & Daniel Wilhelm, 2017. "Optimal data collection for randomized control trials," CeMMAP working papers 45/17, Institute for Fiscal Studies.
    10. Weigel, Jonathan & Balán, Pablo & Bergeron, Augustin & Tourek, Gabriel, 2020. "Local Elites as State Capacity: How City Chiefs Use Local Information to Increase Tax Compliance in the D.R. Congo," CEPR Discussion Papers 15138, C.E.P.R. Discussion Papers.

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    More about this item

    Keywords

    experiment design; robustness; ambiguity aversion; randomization; rerandomization;
    All these keywords.

    JEL classification:

    • C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General
    • D78 - Microeconomics - - Analysis of Collective Decision-Making - - - Positive Analysis of Policy Formulation and Implementation
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

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