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Precise estimation of treatment effects is crucial for evaluatingintervention effectiveness. While deep learning models have exhibited promisingperformance in learning counterfactual representations for treatment effectestimation (TEE), a major limitation in most of these models is that they treatthe entire population as a homogeneous group, overlooking the diversity oftreatment effects across potential subgroups that have varying treatmenteffects. This limitation restricts the ability to precisely estimate treatmenteffects and provide subgroup-specific treatment recommendations. In this paper,we propose a novel treatment effect estimation model, named SubgroupTE, whichincorporates subgroup identification in TEE. SubgroupTE identifiesheterogeneous subgroups with different treatment responses and more preciselyestimates treatment effects by considering subgroup-specific causal effects. Inaddition, SubgroupTE iteratively optimizes subgrouping and treatment effectestimation networks to enhance both estimation and subgroup identification.Comprehensive experiments on the synthetic and semi-synthetic datasets exhibitthe outstanding performance of SubgroupTE compared with the state-of-the-artmodels on treatment effect estimation. Additionally, a real-world studydemonstrates the capabilities of SubgroupTE in enhancing personalized treatmentrecommendations for patients with opioid use disorder (OUD) by advancingtreatment effect estimation with subgroup identification.

SubgroupTE: Advancing Treatment Effect Estimation with Subgroup Identification