Deep learning models have demonstrated promising results in estimating treatment effects (TEE). However, most of them overlook the variations in treatment outcomes among subgroups with distinct characteristics. This limitation hinders their ability to provide accurate estimations and treatment recommendations for specific subgroups. In this study, we introduce a novel neural network-based framework, named SubgroupTE, which incorporates subgroup identification and treatment effect estimation. SubgroupTE identifies diverse subgroups and simultaneously estimates treatment effects for each subgroup, improving the treatment effect estimation by considering the heterogeneity of treatment responses. Comparative experiments on synthetic data show that SubgroupTE outperforms existing models in treatment effect estimation. Furthermore, experiments on a real-world dataset related to opioid use disorder (OUD) demonstrate the potential of our approach to enhance personalized treatment recommendations for OUD patients.

翻译：深度学习模型在估计治疗效果方面已展现出显著成果。然而，大多数模型忽略了具有不同特征亚组之间治疗结局的差异性。这一局限阻碍了模型为特定亚组提供精准估计与治疗建议的能力。本研究提出一种名为SubgroupTE的新型神经网络框架，该框架整合了亚组识别与治疗效果估计功能。SubgroupTE可识别不同亚组，并同时估计各亚组的治疗效果，通过考虑治疗反应的异质性来优化治疗效果估计。基于合成数据的对比实验表明，SubgroupTE在治疗效果估计方面优于现有模型。此外，在阿片类药物使用障碍（OUD）真实世界数据集上的实验证明，我们的方法具有增强阿片类药物使用障碍患者个性化治疗建议的潜力。