Treatment Effect Estimation

Individual treatment effect (ITE) estimation aims to predict how a specific treatment will affect an individual, moving beyond average treatment effects. Current research focuses on improving the accuracy and interpretability of ITE predictions, employing diverse methods such as conformal inference, pairwise training strategies, and deep learning architectures (including neural networks and graph neural networks) to address challenges like confounding and heterogeneous interference. These advancements hold significant promise for personalized medicine, targeted interventions in various fields, and improved decision-making in areas such as e-commerce and education.