There has been a growing interest in the use of machine learning methods for causal inference, which often involves adjusting or reappropriating predictive models, with causality in mind. As an alternative, anomaly detection methods offer a unique lens through which to conduct causal inference, as the presence of a causal effect results in treatment group units that appear anomalous in comparison to the control group. Moreover, anomalous pattern detection intentionally localizes the presence of treatment effects, which has tremendous value when the ultimate goal involves hypothesis generation, understanding causal mechanisms, or targeting subpopulations. As motivation, we will consider the identification of subpopulations in randomized experiments with extremely significant effects, and will consider other (causal and non-casual) settings as time permits.