SubgroupTE: Advancing Treatment Effect Estimation with Subgroup
  Identification
Seungyeon Lee Ruoqi Liu Wenyu Song Lang Li Ping Zhang
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.