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.

中文翻译：

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实验者理论：稳健性，随机性和平衡性

本文研究了避免歧义的决策者进行实验设计的问题，该决策者在主观预期性能与健壮性能保证之间进行权衡。该框架考虑了现实世界中实验者对随机化的偏爱。它还阐明了最佳随机化的情况：当可用样本量较大且鲁棒性很重要时。我们应用我们的模型来阐明重新随机化的实践，该随机化用于改善治疗组和对照组之间的平衡。我们表明，重新随机化会在主观绩效与稳健绩效保证之间做出权衡。但是，强大的性能保证会随着重新随机化的次数而非常缓慢地减少。这表明适度的再随机化有效地扩展了主观表现和健壮性之间可接受的折衷方案。相对于绝对余额目标，针对固定的天平分位数更安全。