  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.
