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

Seungyeon Lee, Ruoqi Liu, Wenyu Song, Ping Zhang

TL;DR

Opioid use disorder (OUD) treatment responses vary across patients, and many existing deep learning approaches for treatment effect estimation neglect subgroup heterogeneity. The authors propose SubgroupTE, a neural framework that jointly learns subgroup identification and subgroup-specific treatment effects through an EM-based training loop, integrating a Transformer-based Feature Representation Network, a Subgrouping Model, and a Subgroup-Informed Prediction Network. The method uses KDE-based centroid alignment to adapt subgroups during training and demonstrates superior TE estimation on synthetic data and actionable subgroup insights on a real-world OUD dataset, enabling more personalized treatment recommendations. Overall, this work advances causal inference in personalized medicine by directly modeling heterogeneity and coupling subgroup discovery with outcome prediction.

Abstract

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.

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

TL;DR

Opioid use disorder (OUD) treatment responses vary across patients, and many existing deep learning approaches for treatment effect estimation neglect subgroup heterogeneity. The authors propose SubgroupTE, a neural framework that jointly learns subgroup identification and subgroup-specific treatment effects through an EM-based training loop, integrating a Transformer-based Feature Representation Network, a Subgrouping Model, and a Subgroup-Informed Prediction Network. The method uses KDE-based centroid alignment to adapt subgroups during training and demonstrates superior TE estimation on synthetic data and actionable subgroup insights on a real-world OUD dataset, enabling more personalized treatment recommendations. Overall, this work advances causal inference in personalized medicine by directly modeling heterogeneity and coupling subgroup discovery with outcome prediction.

Abstract

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.
Paper Structure (15 sections, 10 equations, 4 figures, 2 tables)

This paper contains 15 sections, 10 equations, 4 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related works
  3. Methodology
  4. Problem statement and assumptions
  5. Proposed model
  6. Feature representation network
  7. Subgrouping model
  8. Subgroup-informed prediction network
  9. Optimization
  10. Experiments
  11. Datasets
  12. Experimental setup
  13. Results on synthetic data
  14. Real-world study
  15. Conclusion

Figures (4)

  • Figure 1: Architecture of SubgroupTE.
  • Figure 2: Visualization of the treatment effect distribution for the identified subgroups on (a) synthetic and (b) opioid datasets. Each box signifies the interquartile range, spanning from the 25th to the 75th percentiles of the treatment effect distribution. The whiskers cover the range between the 5th and 95th percentiles.
  • Figure 3: Illustration of the overall design. The index date refers to the first prescription date of the drug. Baseline and follow-up periods include all the dates before and after the index date, respectively.
  • Figure 4: The heatmap of the relative ratios for the variables related to demographics and diagnosis codes across the three subgroups. The relative ratio is computed as $\pi_{k,i}/\sum_{k=1}^{K}\pi_{k,i}$, where $\pi_{k,i}$ is the ratio of the $i$-th variable in the $k$-th subgroup. SMD: Substance-related mental disorders; NSD: Other nervous system disorders; SDB: Spondylosis, intervertebral disc disorders, or other back problems; CTB: Other connective tissue disease.