# Heterogeneous treatment effect estimation with subpopulation identification for personalized medicine in opioid use disorder

arXiv: https://arxiv.org/abs/2401.17027 · HTML: https://mlsanity.com/paper/2401.17027/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 24 references that are in the map.
- First posted on arXiv 30 Jan 2024.

## Builds on (most cited first, 9 of 9)

- [Adapting Neural Networks for the Estimation of Treatment Effects](https://mlsanity.com/paper/1906.02120/) (2019)
- [Learning Counterfactual Representations for Estimating Individual Dose-Response Curves](https://mlsanity.com/paper/1902.00981/) (2019)
- [On Inductive Biases for Heterogeneous Treatment Effect Estimation](https://mlsanity.com/paper/2106.03765/) (2021)
- [VCNet and Functional Targeted Regularization For Learning Causal Effects of Continuous Treatments](https://mlsanity.com/paper/2103.07861/) (2021)
- [Exploring Transformer Backbones for Heterogeneous Treatment Effect Estimation](https://mlsanity.com/paper/2202.01336/) (2022)
- [Interpretable Subgroup Discovery in Treatment Effect Estimation with Application to Opioid Prescribing Guidelines](https://mlsanity.com/paper/1905.03297/) (2019)
- [Robust Recursive Partitioning for Heterogeneous Treatment Effects with Uncertainty Quantification](https://mlsanity.com/paper/2006.07917/) (2020)
- [Identifying Heterogeneous Treatment Effects in Multiple Outcomes using Joint Confidence Intervals](https://mlsanity.com/paper/2212.01437/) (2022)
- [Tree-based Subgroup Discovery In Electronic Health Records: Heterogeneity of Treatment Effects for DTG-containing Therapies](https://mlsanity.com/paper/2208.14329/) (2022)

## Built on by (most cited first, 1 of 1)

- [Towards a Relationship-Aware Transformer for Tabular Data](https://mlsanity.com/paper/2512.07310/) (2025)

## Abstract (arXiv)

Deep learning models have demonstrated promising results in estimating treatment effects (TEE). However, most of them overlook the variations in treatment outcomes among subgroups with distinct characteristics. This limitation hinders their ability to provide accurate estimations and treatment recommendations for specific subgroups. In this study, we introduce a novel neural network-based framework, named SubgroupTE, which incorporates subgroup identification and treatment effect estimation. SubgroupTE identifies diverse subgroups and simultaneously estimates treatment effects for each subgroup, improving the treatment effect estimation by considering the heterogeneity of treatment responses. Comparative experiments on synthetic data show that SubgroupTE outperforms existing models in treatment effect estimation. Furthermore, experiments on a real-world dataset related to opioid use disorder (OUD) demonstrate the potential of our approach to enhance personalized treatment recommendations for OUD patients.