SubgroupTE: Advancing Treatment Effect Estimation with Subgroup Identification
This work addresses the need for precise treatment effect estimation in healthcare, particularly for personalized treatment recommendations, but it is incremental as it builds on existing deep learning models by adding subgroup identification.
The paper tackles the problem of treatment effect estimation by addressing the limitation of treating populations as homogeneous, proposing SubgroupTE to identify heterogeneous subgroups and estimate subgroup-specific effects, resulting in outstanding performance on synthetic and semi-synthetic datasets and enhanced personalized recommendations for opioid use disorder patients.
Precise estimation of treatment effects is crucial for evaluating intervention effectiveness. 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 treat the entire population as a homogeneous group, overlooking the diversity of treatment effects across potential subgroups that have varying treatment effects. This limitation restricts the ability to precisely estimate treatment effects and provide subgroup-specific 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 treatment responses and more precisely estimates treatment effects by considering subgroup-specific causal effects. In addition, SubgroupTE iteratively optimizes subgrouping and treatment effect estimation networks to enhance both estimation and subgroup identification. Comprehensive experiments on the synthetic and semi-synthetic datasets exhibit the outstanding performance of SubgroupTE compared with the state-of-the-art models on treatment effect estimation. Additionally, a real-world study demonstrates the capabilities of SubgroupTE in enhancing personalized treatment recommendations for patients with opioid use disorder (OUD) by advancing treatment effect estimation with subgroup identification.