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[2602.18786] CaliCausalRank: Calibrated Multi-Objective Ad Ranking with Robust Counterfactual Utility Optimization
Summary
CaliCausalRank presents a novel framework for optimizing multi-objective ad ranking systems, addressing challenges like score scale inconsistency and position bias through calibrated training and robust counterfactual utility estimation.
Why It Matters
This research is significant as it tackles critical issues in ad ranking systems that affect performance and user experience. By improving calibration and utility estimation, it enhances the effectiveness of advertising strategies, which is crucial for businesses relying on digital marketing.
Key Takeaways
- CaliCausalRank integrates training-time scale calibration with multi-objective optimization.
- The framework reduces calibration error by 31.6% and improves AUC by 1.1%.
- It employs robust counterfactual estimators for reliable offline evaluation.
- The method maintains consistent performance across different traffic segments.
- This approach addresses common challenges in ad ranking systems, enhancing overall effectiveness.
Computer Science > Machine Learning arXiv:2602.18786 (cs) [Submitted on 21 Feb 2026] 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 View a PDF of the paper titled CaliCausalRank: Calibrated Multi-Objective Ad Ranking with Robust Counterfactual Utility Optimization, by Xikai Yang and 5 other authors View PDF 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) ...