# Causal Rule Sets for Identifying Subgroups with Enhanced Treatment Effect

arXiv: https://arxiv.org/abs/1710.05426 · HTML: https://mlsanity.com/paper/1710.05426/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2017.

## Built on by (most cited first, 15 of 15)

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- [auton-survival: an Open-Source Package for Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Event Data](https://mlsanity.com/paper/2204.07276/) (2022)
- [Counterfactual Phenotyping with Censored Time-to-Events](https://mlsanity.com/paper/2202.11089/) (2022)
- [Fair and Robust Estimation of Heterogeneous Treatment Effects for Policy Learning](https://mlsanity.com/paper/2306.03625/) (2023)
- [Hierarchical and Density-based Causal Clustering](https://mlsanity.com/paper/2411.01250/) (2024)
- [Interpretable Personalized Experimentation](https://mlsanity.com/paper/2111.03267/) (2021)
- [PWSHAP: A Path-Wise Explanation Model for Targeted Variables](https://mlsanity.com/paper/2306.14672/) (2023)
- [A New Causal Rule Learning Approach to Interpretable Estimation of Heterogeneous Treatment Effect](https://mlsanity.com/paper/2310.06746/) (2023)
- [CURLS: Causal Rule Learning for Subgroups with Significant Treatment Effect](https://mlsanity.com/paper/2407.01004/) (2024)
- [Estimating Heterogeneous Causal Effect of Polysubstance Usage on Drug Overdose from Large-Scale Electronic Health Record](https://mlsanity.com/paper/2105.07224/) (2021)
- [Recovering Sparse and Interpretable Subgroups with Heterogeneous Treatment Effects with Censored Time-to-Event Outcomes](https://mlsanity.com/paper/2302.12504/) (2023)
- [SubgroupTE: Advancing Treatment Effect Estimation with Subgroup Identification](https://mlsanity.com/paper/2401.12369/) (2024)
- [MOSIC: Model-Agnostic Optimal Subgroup Identification with Multi-Constraint for Improved Reliability](https://mlsanity.com/paper/2504.20908/) (2025)
- [Identifying treatment response subgroups in observational time-to-event data](https://mlsanity.com/paper/2408.03463/) (2024)
- [Sequential Counterfactual Inference for Temporal Clinical Data: Addressing the Time Traveler Dilemma](https://mlsanity.com/paper/2602.21168/) (2026)

## Abstract (arXiv)

A key question in causal inference analyses is how to find subgroups with elevated treatment effects. This paper takes a machine learning approach and introduces a generative model, Causal Rule Sets (CRS), for interpretable subgroup discovery. A CRS model uses a small set of short decision rules to capture a subgroup where the average treatment effect is elevated. We present a Bayesian framework for learning a causal rule set. The Bayesian model consists of a prior that favors simple models for better interpretability as well as avoiding overfitting, and a Bayesian logistic regression that captures the likelihood of data, characterizing the relation between outcomes, attributes, and subgroup membership. The Bayesian model has tunable parameters that can characterize subgroups with various sizes, providing users with more flexible choices of models from the treatment efficient frontier. We find maximum a posteriori models using iterative discrete Monte Carlo steps in the joint solution space of rules sets and parameters. To improve search efficiency, we provide theoretically grounded heuristics and bounding strategies to prune and confine the search space. Experiments show that the search algorithm can efficiently recover true underlying subgroups. We apply CRS on public and real-world datasets from domains where interpretability is indispensable. We compare CRS with state-of-the-art rule-based subgroup discovery models. Results show that CRS achieved consistently competitive performance on datasets from various domains, represented by high treatment efficient frontiers.