Heterogeneous treatment effect estimation with subpopulation identification for personalized medicine in opioid use disorder
This work addresses the need for accurate personalized medicine in opioid use disorder by improving treatment effect estimation for specific subgroups, though it appears incremental as it builds on existing deep learning methods.
The study tackled the problem of heterogeneous treatment effect estimation by introducing SubgroupTE, a neural network framework that identifies subgroups and estimates treatment effects for each, resulting in outperformance over existing models on synthetic data and potential enhancement of personalized treatment recommendations for opioid use disorder patients.
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.