Uplift Modeling

Uplift modeling aims to predict the incremental impact of an intervention (e.g., a marketing campaign) on individual outcomes, enabling targeted resource allocation and maximizing return on investment. Current research emphasizes improving model accuracy and robustness, particularly for multi-treatment scenarios and under budget constraints, using techniques like tree-boosting, graph neural networks, and mixture-of-experts models. This field is crucial for optimizing marketing strategies, personalized recommendations, and other applications requiring causal inference, with recent work focusing on addressing challenges like data scarcity, heteroskedasticity, and fairness concerns in model evaluation.