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A Theory of Experimenters: Robustness, Randomization, and Balance
Name
aer.20171634.pdf
Description
Published version
Size
695.95 KB
Format
Adobe PDF
Checksum (MD5)
f76124973e06a439c2844e52524f2332
Author(s)
Banerjee, Abhijit V
Chassang, Sylvain
Montero, Sergio
Snowberg, Erik
Date Issued
2020
Journal
American Economic Review
Publisher
American Economic Association
Version
Final published version
Abstract
© 2020 American Economic Association. All rights reserved. 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.
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
10.1257/AER.20171634
