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：[translated abstract in Chinese] 随机实验常因处理效应产生非预期的有害影响而需要提前终止。现有用于判定实验何时提前终止的方法通常应用于聚合数据，未考虑处理效应的异质性。本文研究了异质性群体实验中针对有害效应的早期终止问题。我们首先证明：当处理效应对少数群体产生危害时，现有方法往往无法及时终止实验。继而，我们利用因果机器学习开发了首个具有广泛适用性的异质性早期终止方法——CLASH。通过模拟数据和真实数据验证，我们展示了CLASH在临床试验和A/B测试中均能实现有效的早期终止决策。