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.

翻译：广告排序系统必须同时优化点击率（CTR）、转化率（CVR）、收入及用户体验指标等多个目标。然而，生产系统面临关键挑战：不同流量分区间分数尺度不一致破坏了阈值可迁移性，而点击日志中的位置偏差导致离线与在线指标存在差异。我们提出 CaliCausalRank，一个统一框架，集成了训练时尺度校准、基于约束的多目标优化以及鲁棒的反事实效用估计。我们的方法将分数校准视为首要训练目标而非事后处理，采用拉格朗日松弛法满足约束条件，并利用方差缩减的反事实估计器进行可靠的离线评估。在 Criteo 和 Avazu 数据集上的实验表明，与最佳基线（PairRank）相比，CaliCausalRank 实现了 1.1% 的相对 AUC 提升、31.6% 的校准误差降低和 3.2% 的效用增益，同时在不同流量分区间保持了一致的性能。