[2603.20775] Evaluating Uplift Modeling under Structural Biases: Insights into Metric Stability and Model Robustness

Computer Science > Machine Learning arXiv:2603.20775 (cs) [Submitted on 21 Mar 2026] Title:Evaluating Uplift Modeling under Structural Biases: Insights into Metric Stability and Model Robustness Authors:Yuxuan Yang, Dugang Liu, Yiyan Huang View a PDF of the paper titled Evaluating Uplift Modeling under Structural Biases: Insights into Metric Stability and Model Robustness, by Yuxuan Yang and 2 other authors View PDF HTML (experimental) Abstract:In personalized marketing, uplift models estimate incremental effects by modeling how customer behavior changes under alternative treatments. However, real-world data often exhibit biases - such as selection bias, spillover effects, and unobserved confounding - which adversely affect both estimation accuracy and metric validity. Despite the importance of bias-aware assessment, a lack of systematic studies persists. To bridge this gap, we design a systematic benchmarking framework. Unlike standard predictive tasks, real-world uplift datasets lack counterfactual ground truth, rendering direct metric validation infeasible. Therefore, a semi-synthetic approach serves as a critical enabler for systematic benchmarking, effectively bridging the gap by retaining real-world feature dependencies while providing the ground truth needed to isolate structural biases. Our investigations reveal that: (i) uplift targeting and prediction can manifest as distinct objectives, where proficiency in one does not ensure efficacy in the other; (ii) while m...