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

arXiv: https://arxiv.org/abs/2602.18786 · HTML: https://mlsanity.com/paper/2602.18786/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 36 references that are in the map.
- First posted on arXiv 21 Feb 2026.

## Builds on (most cited first, 22 of 22)

- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://mlsanity.com/paper/1703.04247/) (2017)
- [Deep Interest Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1706.06978/) (2017)
- [DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems](https://mlsanity.com/paper/2008.13535/) (2020)
- [Top-K Off-Policy Correction for a REINFORCE Recommender System](https://mlsanity.com/paper/1812.02353/) (2018)
- [Unbiased Learning-to-Rank with Biased Feedback](https://mlsanity.com/paper/1608.04468/) (2016)
- [Unbiased Learning to Rank with Unbiased Propensity Estimation](https://mlsanity.com/paper/1804.05938/) (2018)
- [ESCM2: Entire Space Counterfactual Multi-Task Model for Post-Click Conversion Rate Estimation](https://mlsanity.com/paper/2204.05125/) (2022)
- [Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning in Targeted Display Advertising](https://mlsanity.com/paper/2105.08489/) (2021)
- [Counterfactual Evaluation of Slate Recommendations with Sequential Reward Interactions](https://mlsanity.com/paper/2007.12986/) (2020)
- [Trustworthy Summarization via Uncertainty Quantification and Risk Awareness in Large Language Models](https://mlsanity.com/paper/2510.01231/) (2025)
- [Dynamic Prompt Fusion for Multi-Task and Cross-Domain Adaptation in LLMs](https://mlsanity.com/paper/2509.18113/) (2025)
- [Structural Generalization for Microservice Routing Using Graph Neural Networks](https://mlsanity.com/paper/2510.15210/) (2025)
- [Multi-Objective Adaptive Rate Limiting in Microservices Using Deep Reinforcement Learning](https://mlsanity.com/paper/2511.03279/) (2025)
- [Deep Learning Approach for Clinical Risk Identification Using Transformer Modeling of Heterogeneous EHR Data](https://mlsanity.com/paper/2511.04158/) (2025)
- [Parameter-Efficient Fine-Tuning with Differential Privacy for Robust Instruction Adaptation in Large Language Models](https://mlsanity.com/paper/2512.06711/) (2025)
- [Deep Q-Learning-Based Intelligent Scheduling for ETL Optimization in Heterogeneous Data Environments](https://mlsanity.com/paper/2512.13060/) (2025)
- [LLM-Centric RAG with Multi-Granular Indexing and Confidence Constraints](https://mlsanity.com/paper/2510.27054/) (2025)
- [Structure-Aware Decoding Mechanisms for Complex Entity Extraction with Large-Scale Language Models](https://mlsanity.com/paper/2512.13980/) (2025)
- [Spatiotemporal Traffic Prediction in Distributed Backend Systems via Graph Neural Networks](https://mlsanity.com/paper/2510.15215/) (2025)
- [Machine Learning Approaches to Clinical Risk Prediction: Multi-Scale Temporal Alignment in Electronic Health Records](https://mlsanity.com/paper/2511.21561/) (2025)
- [Adaptive Human-Computer Interaction Strategies Through Reinforcement Learning in Complex](https://mlsanity.com/paper/2510.27058/) (2025)

## Built on by (most cited first, 1 of 1)

- [Unsupervised Graph Modeling for Anomaly Detection in Accounting Subject Relationships](https://mlsanity.com/paper/2604.26216/) (2026)

## Abstract (arXiv)

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.