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.

翻译：治疗效果的精确实则对评估干预措施的有效性至关重要。尽管深度学习模型在学习反事实表征以进行治疗效果估计方面展现出令人期待的性能，但这些模型的一个主要局限在于，它们将整个人群视为同质群体，忽略了可能存在不同治疗效果的各亚组之间治疗效果的异质性。这一限制制约了精确估计治疗效果并提供针对特定亚组治疗建议的能力。本文提出一种新颖的治疗效果估计模型SubgroupTE，该模型将亚组识别融入治疗效果估计过程。SubgroupTE能够识别具有不同治疗反应的异质性亚组，并通过考虑亚组特异性因果效应来更精确地估计治疗效果。此外，SubgroupTE通过迭代优化亚组划分与治疗效果估计网络，同时提升两项任务的性能。在合成和半合成数据集上的综合实验表明，与当前最先进的模型相比，SubgroupTE在治疗效果估计方面表现出卓越性能。一项真实世界研究进一步展示了SubgroupTE通过结合亚组识别优化治疗效果估计，从而增强对阿片类药物使用障碍患者个性化治疗建议的能力。