Randomized experiments often need to be stopped prematurely due to the treatment having an unintended harmful effect. Existing methods that determine when to stop an experiment early are typically applied to the data in aggregate and do not account for treatment effect heterogeneity. In this paper, we study the early stopping of experiments for harm on heterogeneous populations. We first establish that current methods often fail to stop experiments when the treatment harms a minority group of participants. We then use causal machine learning to develop CLASH, the first broadly-applicable method for heterogeneous early stopping. We demonstrate CLASH's performance on simulated and real data and show that it yields effective early stopping for both clinical trials and A/B tests.

翻译：随机实验常因处理产生非预期有害效应而需要提前终止。现有确定实验提前停止时机的方法通常应用于聚合数据，未考虑处理效应的异质性。本文研究针对异质性总体的实验早期停止问题。我们首先证明，当处理对少数参与者群体造成伤害时，现有方法往往无法及时停止实验。进而利用因果机器学习提出CLASH——首个广泛适用的异质性早期停止方法。我们在模拟数据和真实数据上验证了CLASH的性能，表明该方法在临床试验和A/B测试中均能实现有效的早期停止。