# Model-based Recursive Partitioning for Subgroup Analyses

arXiv: https://arxiv.org/abs/1605.04078 · HTML: https://mlsanity.com/paper/1605.04078/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 May 2016.

## Built on by (most cited first, 22 of 22)

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- [Heterogeneous Treatment Effect Estimation for Observational Data using Model-based Forests](https://mlsanity.com/paper/2210.02836/) (2022)
- [A New Causal Rule Learning Approach to Interpretable Estimation of Heterogeneous Treatment Effect](https://mlsanity.com/paper/2310.06746/) (2023)
- [Optimal subgroup selection](https://mlsanity.com/paper/2109.01077/) (2021)
- [Tree-based Subgroup Discovery In Electronic Health Records: Heterogeneity of Treatment Effects for DTG-containing Therapies](https://mlsanity.com/paper/2208.14329/) (2022)
- [Targeting relative risk heterogeneity with causal forests](https://mlsanity.com/paper/2309.15793/) (2023)
- [Subgroup analysis methods for time-to-event outcomes in heterogeneous randomized controlled trials](https://mlsanity.com/paper/2401.11842/) (2024)
- [Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling](https://mlsanity.com/paper/2505.01145/) (2025)
- [Learning Subgroups with Maximum Treatment Effects without Causal Heuristics](https://mlsanity.com/paper/2511.20189/) (2025)
- [De-paradox Tree: Breaking Down Simpson's Paradox via A Kernel-Based Partition Algorithm](https://mlsanity.com/paper/2603.02174/) (2026)
- [Machine learning in the social and health sciences](https://mlsanity.com/paper/2106.10716/) (2021)
- [Model-based recursive partitioning for discrete event times](https://mlsanity.com/paper/2209.06592/) (2022)
- [Toward Transparent Sequence Models with Model-Based Tree Markov Model](https://mlsanity.com/paper/2307.15367/) (2023)
- [Subgroup Analysis via Model-based Rule Forest](https://mlsanity.com/paper/2408.15057/) (2024)
- [Bayesian Supervised Causal Clustering](https://mlsanity.com/paper/2603.05288/) (2026)
- [Differential Subgroup Discovery: Characterizing Where Two Populations Differ, and Why](https://mlsanity.com/paper/2604.27741/) (2026)
- [Simultaneous Latent Budget Trees for Stratified Classification](https://mlsanity.com/paper/2606.13295/) (2026)
- [A Bregman Perspective on Classification and Regression Trees](https://mlsanity.com/paper/2606.13984/) (2026)
- [Co-Supervised Tree Synthesis for Interpretable Subgroup Identification and Honest Treatment Effect Inference in Randomized Trials](https://mlsanity.com/paper/2610.03927/) (2026)

## Abstract (arXiv)

The identification of patient subgroups with differential treatment effects is the first step towards individualised treatments. A current draft guideline by the EMA discusses potentials and problems in subgroup analyses and formulated challenges to the development of appropriate statistical procedures for the data-driven identification of patient subgroups. We introduce model-based recursive partitioning as a procedure for the automated detection of patient subgroups that are identifiable by predictive factors. The method starts with a model for the overall treatment effect as defined for the primary analysis in the study protocol and uses measures for detecting parameter instabilities in this treatment effect. The procedure produces a segmented model with differential treatment parameters corresponding to each patient subgroup. The subgroups are linked to predictive factors by means of a decision tree. The method is applied to the search for subgroups of patients suffering from amyotrophic lateral sclerosis that differ with respect to their Riluzole treatment effect, the only currently approved drug for this disease.