An Algorithm for Identifying Interpretable Subgroups With Elevated Treatment Effects

TL;DR

This paper presents an algorithm that identifies interpretable subgroups with high treatment effects using rule sets, aiding decision-making and understanding in treatment effect analysis.

Contribution

The method introduces a novel objective balancing subgroup size and effect size, producing Pareto optimal rule sets for interpretable subgroup identification.

Findings

Produces interpretable rule sets capturing high-order interactions

Offers a Pareto frontier of optimal subgroups balancing size and effect

Demonstrates utility through simulated and empirical examples

Abstract

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.