arXiv 13 Jul 2025 · Statistics — Machine Learning
arXiv:2507.09494 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Athey \ Wager (2021) Policy learning with observational data | 0.843 | 3 | 3 | 100% |
| 2 | Wager \ Athey (2018) Estimation and inference of heterogeneous treatment effects using random forests | 0.843 | 3 | 3 | 100% |
| 3 | Wang \ Rudin (2022) Causal rule sets for identifying subgroups with enhanced treatment effects | 0.644 | 2 | 2 | 100% |
| 4 | Miettinen (1999) Nonlinear multiobjective optimization | 0.511 | 2 | 1 | 100% |
| 5 | Athey \ Imbens (2016) Recursive partitioning for heterogeneous causal effects | 0.405 | 1 | 1 | 100% |
| 6 | Chiu \ Xu (2023) Bayesian rule set: a quantitative alternative to qualitative comparative analysis | 0.405 | 1 | 1 | 100% |
| 7 | Czyzżak \ Jaszkiewicz (1998) Pareto simulated annealing–-a metaheuristic technique for multiple-objective combinatorial optimization | 0.405 | 1 | 1 | 100% |
| 8 | Doshi-Velez \ Kim (2017) Towards a rigorous science of interpretable machine learning | 0.405 | 1 | 1 | 100% |
| 9 | Foster, Taylor \ Ruberg (2011) Subgroup identification from randomized clinical trial data | 0.405 | 1 | 1 | 100% |
| 10 | Grimmer, Messing \ Westwood (2017) Estimating heterogeneous treatment effects and the effects of heterogeneous treatments with ensemble methods | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 24 scored citations.