A Theory of Experimenters: Robustness, Randomization, and Balance
By Abhijit Banerjee, Sylvain Chassang, Sergio Montero, Erik Snowberg. The American Economic Review, 2020
78 citations recorded by the source corpus.
This paper studies the problem of experiment design by an ambiguity-averse decision-maker who trades off subjective expected performance against robust performance guarantees. This framework accounts for real-world experimenters’ preference for randomization. It also clarifies the circumstances in which randomization is optimal: when the available sample size is large and robustness is an important concern. We apply our model to shed light on the practice of rerandomization, used to improve balance across treatment and control groups. We show that rerandomization creates a trade-off between subjective performance and robust performance guarantees. However, robust performance guarantees diminish very slowly with the number of rerandomizations. This suggests that moderate levels of rerandomization usefully expand the set of acceptable compromises between subjective performance and robustness. Targeting a fixed quantile of balance is safer than targeting an absolute balance objective. (JEL C90, D81)
Subfields: General Decision Sciences, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/dd3231dc1ac12350fea08063e7ca1d3afeaa3dd5
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In General Decision Sciences
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Citations in the corpus
- Covariate-adjusted Fisher randomization tests for the average treatment effect (2020, background): “Banerjee et al. (2020) discussed the pros and cons of such experimental design.”