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insight - Uplift Modeling with Graph Neural Networks
Graph Neural Network with Two Uplift Estimators for Label-Scarcity Individual Uplift Modeling
The author proposes a novel framework, GNUM, utilizing graph neural networks and two uplift estimators to address label scarcity in individual uplift modeling.
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%.