Causal Rule Ensemble: Interpretable Discovery and Inference of Heterogeneous Treatment Effects

In health and social sciences, it is critically important to identify subgroups of the study population where a treatment has notable heterogeneity in the causal effects with respect to the average treatment effect. Data-driven discovery of heterogeneous treatment effects (HTE) via decision tree methods has been proposed for this task. Despite its high interpretability, the single-tree discovery of HTE tends to be highly unstable and to find an oversimplified representation of treatment heterogeneity. To accommodate these shortcomings, we propose Causal Rule Ensemble (CRE), a new method to discover heterogeneous subgroups through an ensemble-of-trees approach. CRE has the following features: 1) provides an interpretable representation of the HTE; 2) allows extensive exploration of complex heterogeneity patterns; and 3) guarantees high stability in the discovery. The discovered subgroups are defined in terms of interpretable decision rules, and we develop a general two-stage approach for subgroup-specific conditional causal effects estimation, providing theoretical guarantees. Via simulations, we show that the CRE method has a strong discovery ability and a competitive estimation performance when compared to state-of-the-art techniques. Finally, we apply CRE to discover subgroups most vulnerable to the effects of exposure to air pollution on mortality for 35.3 million Medicare beneficiaries across the contiguous U.S.