A Theory of Experimenters / Abhijit Banerjee, Sylvain Chassang, Sergio Montero, Erik Snowberg.

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Banerjee, Abhijit

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TextSeries: Working Paper Series (National Bureau of Economic Research) ; no. w23867.Publication details: Cambridge, Mass. National Bureau of Economic Research 2017.Description: 1 online resource: illustrations (black and white)Subject(s):

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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.

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Item type	Home library	Collection	Call number	Status	Date due	Barcode	Item holds
Working Paper	Biblioteca Digital	Colección NBER	nber w23867 (Browse shelf(Opens below))	Not For Loan

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September 2017.

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.