A Theory of Experimenters: Robustness, Randomization, and Balance
This paper studies the problem of experiment design by an ambiguity-averse decisionmaker 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 tradeoff 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 a safer approach than targeting an absolute balance objective.
Keywords: experiment design; experimenters; Theory; Performance;
Citation:
Banerjee, Abhijit, Sylvain Chassang, Sergio Montero, and Erik Snowberg. "A Theory of Experimenters: Robustness, Randomization, and Balance." American Economic Review 110, no. 4 (April 2020). View Details