  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.
