  In health and social sciences, it is critically important to identify
subgroups of the study population where there is notable heterogeneity of
treatment effects (HTE) with respect to the population average. Decision trees
have been proposed and commonly adopted for the data-driven discovery of HTE
due to their high level of interpretability. However, single-tree discovery of
HTE can be unstable and oversimplified. This paper introduces the Causal Rule
Ensemble (CRE), a new method for HTE discovery and estimation using an
ensemble-of-trees approach. CRE offers several key features, including 1) an
interpretable representation of the HTE; 2) the ability to explore complex
heterogeneity patterns; and 3) high stability in subgroups discovery. The
discovered subgroups are defined in terms of interpretable decision rules.
Estimation of subgroup-specific causal effects is performed via a two-stage
approach, for which we provide theoretical guarantees. Through simulations, we
show that the CRE method is highly competitive compared to state-of-the-art
techniques. Finally, we apply CRE to discover the heterogeneous health effects
of exposure to air pollution on mortality for 35.3 million Medicare
beneficiaries across the contiguous U.S.
