An Algorithm for Identifying Interpretable Subgroups With Elevated Treatment Effects

We introduce an algorithm for identifying interpretable subgroups with elevated treatment effects, given an estimate of individual or conditional average treatment effects (CATE). Subgroups are characterized by "rule sets"--easy-to-understand statements of the form (Condition A AND Condition B) OR (Condition C) --which can capture high-order interactions while retaining interpretability. Our method complements existing approaches for estimating the CATE, which often produce high dimensional and uninterpretable results, by summarizing and extracting critical information from fitted models to aid decision making, policy implementation, and scientific understanding. We propose an objective function that trades-off subgroup size and effect size, and varying the hyperparameter that controls this trade-off results in a "frontier" of Pareto optimal rule sets, none of which dominates the others across all criteria. Valid inference is achievable through sample splitting. We demonstrate the utility and limitations of our method using simulated and empirical examples. In causal inference, average treatment effects (ATE) and average treatment effects on the treated (ATT) are the estimands that garner the most interest. Even if the effect of a treatment is known to be positive on average, it can vary greatly across individuals; some individuals will benefit, but some may experience no effect, and others may even be hurt.