# Identifying Heterogeneous Treatment Effects in Multiple Outcomes using Joint Confidence Intervals

arXiv: https://arxiv.org/abs/2212.01437 · HTML: https://mlsanity.com/paper/2212.01437/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 31 references that are in the map.
- First posted on arXiv 2 Dec 2022.

## Builds on (most cited first, 7 of 7)

- [Distribution-Free Predictive Inference For Regression](https://mlsanity.com/paper/1604.04173/) (2016)
- [Conformalized Quantile Regression](https://mlsanity.com/paper/1905.03222/) (2019)
- [Recursive Partitioning for Heterogeneous Causal Effects](https://mlsanity.com/paper/1504.01132/) (2015)
- [Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes](https://mlsanity.com/paper/1704.02801/) (2017)
- [Model-based Recursive Partitioning for Subgroup Analyses](https://mlsanity.com/paper/1605.04078/) (2016)
- [Efficient Heterogeneous Treatment Effect Estimation With Multiple Experiments and Multiple Outcomes](https://mlsanity.com/paper/2206.04907/) (2022)
- [Interpretable Personalized Experimentation](https://mlsanity.com/paper/2111.03267/) (2021)

## Built on by (most cited first, 4 of 4)

- [Conditional Conformal Risk Adaptation](https://mlsanity.com/paper/2504.07611/) (2025)
- [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)
- [Causal Clustering for Conditional Average Treatment Effects Estimation and Subgroup Discovery](https://mlsanity.com/paper/2509.05775/) (2025)

## Abstract (arXiv)

Heterogeneous treatment effects (HTEs) are commonly identified during randomized controlled trials (RCTs). Identifying subgroups of patients with similar treatment effects is of high interest in clinical research to advance precision medicine. Often, multiple clinical outcomes are measured during an RCT, each having a potentially heterogeneous effect. Recently there has been high interest in identifying subgroups from HTEs, however, there has been less focus on developing tools in settings where there are multiple outcomes. In this work, we propose a framework for partitioning the covariate space to identify subgroups across multiple outcomes based on the joint CIs. We test our algorithm on synthetic and semi-synthetic data where there are two outcomes, and demonstrate that our algorithm is able to capture the HTE in both outcomes simultaneously.