  Overlap between treatment groups is required for non-parametric estimation of
causal effects. If a subgroup of subjects always receives the same
intervention, we cannot estimate the effect of intervention changes on that
subgroup without further assumptions. When overlap does not hold globally,
characterizing local regions of overlap can inform the relevance of causal
conclusions for new subjects, and can help guide additional data collection. To
have impact, these descriptions must be interpretable for downstream users who
are not machine learning experts, such as policy makers. We formalize overlap
estimation as a problem of finding minimum volume sets subject to coverage
constraints and reduce this problem to binary classification with Boolean rule
classifiers. We then generalize this method to estimate overlap in off-policy
policy evaluation. In several real-world applications, we demonstrate that
these rules have comparable accuracy to black-box estimators and provide
intuitive and informative explanations that can inform policy making.
