Leveraging Graph Neural Networks to Predict Treatment Effects with Limited Supervision

The core message of this paper is to develop a novel modular framework, based on graph neural networks (GNNs) and active learning, to address the need for limited supervision in uplift modeling, moving from the standard "70%-80%" train set rule down to 5%-20%.