CaliCausalRank: Calibrated Multi-Objective Ad Ranking with Robust Counterfactual Utility Optimization

• URL: http://arxiv.org/abs/2602.18786v1
• Date: Sat, 21 Feb 2026 10:35:12 GMT
• Title: CaliCausalRank: Calibrated Multi-Objective Ad Ranking with Robust Counterfactual Utility Optimization
• Authors: Xikai Yang, Sebastian Sun, Yilin Li, Yue Xing, Ming Wang, Yang Wang,
• Abstract summary: CaliCausalRank is a framework that integrates training-time scale calibration, constraint-based multi-objective optimization, and robust counterfactual utility estimation.<n>Our approach treats score calibration as a first-class training objective rather than post-hoc processing, employs Lagrangian relaxation for constraint satisfaction, and achieves variance-reduced counterfactual estimators for reliable offline evaluation.
• Score: 9.601427882648116
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Ad ranking systems must simultaneously optimize multiple objectives including click-through rate (CTR), conversion rate (CVR), revenue, and user experience metrics. However, production systems face critical challenges: score scale inconsistency across traffic segments undermines threshold transferability, and position bias in click logs causes offline-online metric discrepancies. We propose CaliCausalRank, a unified framework that integrates training-time scale calibration, constraint-based multi-objective optimization, and robust counterfactual utility estimation. Our approach treats score calibration as a first-class training objective rather than post-hoc processing, employs Lagrangian relaxation for constraint satisfaction, and utilizes variance-reduced counterfactual estimators for reliable offline evaluation. Experiments on the Criteo and Avazu datasets demonstrate that CaliCausalRank achieves 1.1% relative AUC improvement, 31.6% calibration error reduction, and 3.2% utility gain compared to the best baseline (PairRank) while maintaining consistent performance across different traffic segments.

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