
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%.


coremsg

leveraging-graph-neural-networks-to-predict-treatment-effects-with-limited-supervision


Leveraging Graph Neural Networks to Predict Treatment Effects with Limited Supervision


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The author proposes a novel framework, GNUM, utilizing graph neural networks and two uplift estimators to address label scarcity in individual uplift modeling.


graph-neural-network-with-two-uplift-estimators-for-label-scarcity-individual-uplift-modeling


Graph Neural Network with Two Uplift Estimators for Label-Scarcity Individual Uplift Modeling
