# Robust Recursive Partitioning for Heterogeneous Treatment Effects with Uncertainty Quantification

arXiv: https://arxiv.org/abs/2006.07917 · HTML: https://mlsanity.com/paper/2006.07917/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2020.

## Built on by (most cited first, 12 of 12)

- [Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression](https://mlsanity.com/paper/2102.08208/) (2021)
- [Quantifying Aleatoric Uncertainty of the Treatment Effect: A Novel Orthogonal Learner](https://mlsanity.com/paper/2411.03387/) (2024)
- [Identification of Subgroups With Similar Benefits in Off-Policy Policy Evaluation](https://mlsanity.com/paper/2111.14272/) (2021)
- [Hybrid Meta-learners for Estimating Heterogeneous Treatment Effects](https://mlsanity.com/paper/2506.13680/) (2025)
- [Interpretable Personalized Experimentation](https://mlsanity.com/paper/2111.03267/) (2021)
- [SubgroupTE: Advancing Treatment Effect Estimation with Subgroup Identification](https://mlsanity.com/paper/2401.12369/) (2024)
- [Heterogeneous treatment effect estimation with subpopulation identification for personalized medicine in opioid use disorder](https://mlsanity.com/paper/2401.17027/) (2024)
- [A Deep Subgrouping Framework for Precision Drug Repurposing via Emulating Clinical Trials on Real-world Patient Data](https://mlsanity.com/paper/2412.20373/) (2024)
- [Causal Clustering for Conditional Average Treatment Effects Estimation and Subgroup Discovery](https://mlsanity.com/paper/2509.05775/) (2025)
- [Robust and Heterogenous Odds Ratio: Estimating Price Sensitivity for Unbought Items](https://mlsanity.com/paper/2106.11389/) (2021)
- [Probabilistic Shapley Value Modeling and Inference](https://mlsanity.com/paper/2402.04211/) (2024)
- [Spatial-Aware Decision-Making with Ring Attractors in Reinforcement Learning Systems](https://mlsanity.com/paper/2410.03119/) (2024)

## Abstract (arXiv)

Subgroup analysis of treatment effects plays an important role in applications from medicine to public policy to recommender systems. It allows physicians (for example) to identify groups of patients for whom a given drug or treatment is likely to be effective and groups of patients for which it is not. Most of the current methods of subgroup analysis begin with a particular algorithm for estimating individualized treatment effects (ITE) and identify subgroups by maximizing the difference across subgroups of the average treatment effect in each subgroup. These approaches have several weaknesses: they rely on a particular algorithm for estimating ITE, they ignore (in)homogeneity within identified subgroups, and they do not produce good confidence estimates. This paper develops a new method for subgroup analysis, R2P, that addresses all these weaknesses. R2P uses an arbitrary, exogenously prescribed algorithm for estimating ITE and quantifies the uncertainty of the ITE estimation, using a construction that is more robust than other methods. Experiments using synthetic and semi-synthetic datasets (based on real data) demonstrate that R2P constructs partitions that are simultaneously more homogeneous within groups and more heterogeneous across groups than the partitions produced by other methods. Moreover, because R2P can employ any ITE estimator, it also produces much narrower confidence intervals with a prescribed coverage guarantee than other methods.