Towards Robust Offline Evaluation: A Causal and Information Theoretic Framework for Debiasing Ranking Systems

• URL: http://arxiv.org/abs/2504.03997v1
• Date: Fri, 04 Apr 2025 23:52:57 GMT
• Title: Towards Robust Offline Evaluation: A Causal and Information Theoretic Framework for Debiasing Ranking Systems
• Authors: Seyedeh Baharan Khatami, Sayan Chakraborty, Ruomeng Xu, Babak Salimi,
• Abstract summary: offline evaluation of retrieval-ranking systems is crucial for developing high-performing models.<n>We propose a novel framework for robust offline evaluation of retrieval-ranking systems.<n>Our contributions include (1) a causal formulation for addressing offline evaluation biases, (2) a system-agnostic debiasing framework, and (3) empirical validation of its effectiveness.
• Score: 6.540293515339111
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Evaluating retrieval-ranking systems is crucial for developing high-performing models. While online A/B testing is the gold standard, its high cost and risks to user experience require effective offline methods. However, relying on historical interaction data introduces biases-such as selection, exposure, conformity, and position biases-that distort evaluation metrics, driven by the Missing-Not-At-Random (MNAR) nature of user interactions and favoring popular or frequently exposed items over true user preferences. We propose a novel framework for robust offline evaluation of retrieval-ranking systems, transforming MNAR data into Missing-At-Random (MAR) through reweighting combined with black-box optimization, guided by neural estimation of information-theoretic metrics. Our contributions include (1) a causal formulation for addressing offline evaluation biases, (2) a system-agnostic debiasing framework, and (3) empirical validation of its effectiveness. This framework enables more accurate, fair, and generalizable evaluations, enhancing model assessment before deployment.

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