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Subgroup analysis has emerged as an important tool to identify unknown subgroup memberships in the presence of heterogeneity. However, much of the existing work focused on the low-dimensional scenario where only a few candidate variables are considered for modeling the subgroup membership. In this paper, we propose a two-component structured mixture model with a Bayesian variable selection approach for identifying predictive and prognostic variables separately in the high-dimensional setting. By employing spike and slab priors, we achieve the selection of predictive and prognostic variables and the estimation of the treatment effect in the selected subgroup simultaneously. We establish theoretical properties by showing strong variable selection consistency and posterior contraction behavior of our method, and demonstrate its performance using simulation studies. Finally, we apply the proposed method to data from the National Supported Work and the AIDS Clinical Trials Group 320 study, identifying predictive and prognostic variables associated with subgroups exhibiting differential treatment effects.
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This content will become publicly available on March 19, 2026
SubgroupTE: Advancing Treatment Effect Estimation with Subgroup Identification
Precise estimation of treatment effects is crucial for accurately evaluating the intervention. While deep learning models have exhibited promising performance in learning counterfactual representations for treatment effect estimation (TEE), a major limitation in most of these models is that they often overlook the diversity of treatment effects across potential subgroups that have varying treatment effects and characteristics, treating the entire population as a homogeneous group. This limitation restricts the ability to precisely estimate treatment effects and provide targeted treatment recommendations. In this paper, we propose a novel treatment effect estimation model, named SubgroupTE, which incorporates subgroup identification in TEE. SubgroupTE identifies heterogeneous subgroups with different responses and more precisely estimates treatment effects by considering subgroup-specific treatment effects in the estimation process. In addition, we introduce an expectation–maximization (EM)-based training process that iteratively optimizes estimation and subgrouping networks to improve both estimation and subgroup identification. Comprehensive experiments on the synthetic and semi-synthetic datasets demonstrate the outstanding performance of SubgroupTE compared to the existing works for treatment effect estimation and subgrouping models. Additionally, a real-world study demonstrates the capabilities of SubgroupTE in enhancing targeted treatment recommendations for patients with opioid use disorder (OUD) by incorporating subgroup identification with treatment effect estimation.
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- Award ID(s):
- 2145625
- PAR ID:
- 10578166
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- ACM Transactions on Intelligent Systems and Technology
- ISSN:
- 2157-6904
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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