# Off-Policy Evaluation for Ranking Policies under Deterministic Logging Policies

arXiv: https://arxiv.org/abs/2603.21485 · HTML: https://mlsanity.com/paper/2603.21485/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 22 references that are in the map.
- First posted on arXiv 23 Mar 2026.

## Builds on (most cited first, 16 of 16)

- [Doubly Robust Policy Evaluation and Optimization](https://mlsanity.com/paper/1503.02834/) (2015)
- [KuaiRec: A Fully-observed Dataset and Insights for Evaluating Recommender Systems](https://mlsanity.com/paper/2202.10842/) (2022)
- [A Review of Off-Policy Evaluation in Reinforcement Learning](https://mlsanity.com/paper/2212.06355/) (2022)
- [Doubly robust off-policy evaluation with shrinkage](https://mlsanity.com/paper/1907.09623/) (2019)
- [Off-policy Bandits with Deficient Support](https://mlsanity.com/paper/2006.09438/) (2020)
- [Off-Policy Evaluation for Large Action Spaces via Embeddings](https://mlsanity.com/paper/2202.06317/) (2022)
- [Counterfactual Evaluation of Slate Recommendations with Sequential Reward Interactions](https://mlsanity.com/paper/2007.12986/) (2020)
- [Off-Policy Evaluation for Large Action Spaces via Conjunct Effect Modeling](https://mlsanity.com/paper/2305.08062/) (2023)
- [Offline Evaluation of Ranking Policies with Click Models](https://mlsanity.com/paper/1804.10488/) (2018)
- [Doubly Robust Off-Policy Evaluation for Ranking Policies under the Cascade Behavior Model](https://mlsanity.com/paper/2202.01562/) (2022)
- [Policy-Adaptive Estimator Selection for Off-Policy Evaluation](https://mlsanity.com/paper/2211.13904/) (2022)
- [Off-Policy Evaluation of Slate Bandit Policies via Optimizing Abstraction](https://mlsanity.com/paper/2402.02171/) (2024)
- [Off-Policy Evaluation of Ranking Policies under Diverse User Behavior](https://mlsanity.com/paper/2306.15098/) (2023)
- [Marginal Density Ratio for Off-Policy Evaluation in Contextual Bandits](https://mlsanity.com/paper/2312.01457/) (2023)
- [Effective Off-Policy Evaluation and Learning in Contextual Combinatorial Bandits](https://mlsanity.com/paper/2408.11202/) (2024)
- [Clustering Context in Off-Policy Evaluation](https://mlsanity.com/paper/2502.21304/) (2025)

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

- [When Can You Trust Offline Evaluation of Equal-Cost Top-k Allocation? A Controlled, Reproducible Benchmark and Practitioner's Guide](https://mlsanity.com/paper/2608.12489/) (2026)

## Abstract (arXiv)

Off-Policy Evaluation (OPE) is an important practical problem in algorithmic ranking systems, where the goal is to estimate the expected performance of a new ranking policy using only offline logged data collected under a different, logging policy. Existing estimators, such as the ranking-wise and position-wise inverse propensity score (IPS) estimators, require the data collection policy to be sufficiently stochastic and suffer from severe bias when the logging policy is fully deterministic. In this paper, we propose novel estimators, Click-based Inverse Propensity Score (CIPS), exploiting the intrinsic stochasticity of user click behavior to address this challenge. Unlike existing methods that rely on the stochasticity of the logging policy, our approach uses click probability as a new form of importance weighting, enabling low-bias OPE even under deterministic logging policies where existing methods incur substantial bias. We provide theoretical analyses of the bias and variance properties of the proposed estimators and show, through synthetic and real-world experiments, that our estimators achieve significantly lower bias compared to strong baselines, for a range of experimental settings with completely deterministic logging policies.