Heterogeneous treatment effect estimation with subpopulation
  identification for personalized medicine in opioid use disorder
Seungyeon Lee Ruoqi Liu Wenyu Song Ping Zhang
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.