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

• URL: http://arxiv.org/abs/2603.20775v1
• Date: Sat, 21 Mar 2026 11:54:25 GMT
• Title: Evaluating Uplift Modeling under Structural Biases: Insights into Metric Stability and Model Robustness
• Abstract summary: In personalized marketing, uplift models estimate incremental effects by modeling how customer behavior changes under alternative treatments.<n>Real-world data often exhibit biases, such as selection bias, spillover effects, and unobserved confounding.
• Score: 8.135022024189306
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 many models exhibit inconsistent performance under diverse biases, TARNet shows notable robustness, providing insights for subsequent model design; (iii) evaluation metric stability is linked to mathematical alignment with the ATE, suggesting that ATE-approximating metrics yield more consistent model rankings under structural data imperfections. These findings suggest the need for more robust uplift models and metrics. Code will be released upon acceptance.

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