# Offline Evaluation of Ranking Policies with Click Models

arXiv: https://arxiv.org/abs/1804.10488 · HTML: https://mlsanity.com/paper/1804.10488/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Apr 2018.

## Built on by (most cited first, 30 of 32)

- [Off-Policy Evaluation for Large Action Spaces via Embeddings](https://mlsanity.com/paper/2202.06317/) (2022)
- [Off-Policy Evaluation for Large Action Spaces via Conjunct Effect Modeling](https://mlsanity.com/paper/2305.08062/) (2023)
- [Doubly Robust Off-Policy Evaluation for Ranking Policies under the Cascade Behavior Model](https://mlsanity.com/paper/2202.01562/) (2022)
- [Doubly-Robust Estimation for Correcting Position-Bias in Click Feedback for Unbiased Learning to Rank](https://mlsanity.com/paper/2203.17118/) (2022)
- [A Survey on LLM-based Conversational User Simulation](https://mlsanity.com/paper/2604.24977/) (2026)
- [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)
- [Control Variates for Slate Off-Policy Evaluation](https://mlsanity.com/paper/2106.07914/) (2021)
- [Multi-Task Off-Policy Learning from Bandit Feedback](https://mlsanity.com/paper/2212.04720/) (2022)
- [Probabilistic Rank and Reward: A Scalable Model for Slate Recommendation](https://mlsanity.com/paper/2208.06263/) (2022)
- [Effective Off-Policy Evaluation and Learning in Contextual Combinatorial Bandits](https://mlsanity.com/paper/2408.11202/) (2024)
- [Off-Policy Evaluation from Logged Human Feedback](https://mlsanity.com/paper/2406.10030/) (2024)
- [Off-Policy Evaluation in Embedded Spaces](https://mlsanity.com/paper/2203.02807/) (2022)
- [Pessimistic Off-Policy Optimization for Learning to Rank](https://mlsanity.com/paper/2206.02593/) (2022)
- [Offline Evaluation of Reward-Optimizing Recommender Systems: The Case of Simulation](https://mlsanity.com/paper/2209.08642/) (2022)
- [Distributional Off-Policy Evaluation for Slate Recommendations](https://mlsanity.com/paper/2308.14165/) (2023)
- [Double Clipping: Less-Biased Variance Reduction in Off-Policy Evaluation](https://mlsanity.com/paper/2309.01120/) (2023)
- [Ranking Across Different Content Types: The Robust Beauty of Multinomial Blending](https://mlsanity.com/paper/2408.09168/) (2024)
- [Additive Control Variates Dominate Self-Normalisation in Off-Policy Evaluation](https://mlsanity.com/paper/2602.14914/) (2026)
- [Low-variance estimation in the Plackett-Luce model via quasi-Monte Carlo sampling](https://mlsanity.com/paper/2205.06024/) (2022)
- [Off-policy evaluation for learning-to-rank via interpolating the item-position model and the position-based model](https://mlsanity.com/paper/2210.09512/) (2022)
- [The Importance of Causality in Decision Making: A Perspective on Recommender Systems](https://mlsanity.com/paper/2410.01822/) (2024)
- [Unidentified and Confounded? Understanding Two-Tower Models for Unbiased Learning to Rank](https://mlsanity.com/paper/2506.20501/) (2025)
- [Off-Policy Evaluation for Ranking Policies under Deterministic Logging Policies](https://mlsanity.com/paper/2603.21485/) (2026)
- [Inverse Propensity Score based offline estimator for deterministic ranking lists using position bias](https://mlsanity.com/paper/2208.14980/) (2022)
- [Off-Policy Evaluation for Recommendations with Missing-Not-At-Random Rewards](https://mlsanity.com/paper/2502.08993/) (2025)
- [Off-Policy Evaluation of Ranking Policies via Embedding-Space User Behavior Modeling](https://mlsanity.com/paper/2506.00446/) (2025)
- [Exploitation Over Exploration: Unmasking the Bias in Linear Bandit Recommender Offline Evaluation](https://mlsanity.com/paper/2507.18756/) (2025)
- [Accelerating A/B-Tests with Counterfactual Estimation: Reducing Variance through Policy Overlap](https://mlsanity.com/paper/2607.14604/) (2026)
- [Adaptive Doubly Robust Off-Policy Evaluation for Ranking Policies under Diverse User Behavior](https://mlsanity.com/paper/2608.29600/) (2026)

## Abstract (arXiv)

Many web systems rank and present a list of items to users, from recommender systems to search and advertising. An important problem in practice is to evaluate new ranking policies offline and optimize them before they are deployed. We address this problem by proposing evaluation algorithms for estimating the expected number of clicks on ranked lists from historical logged data. The existing algorithms are not guaranteed to be statistically efficient in our problem because the number of recommended lists can grow exponentially with their length. To overcome this challenge, we use models of user interaction with the list of items, the so-called click models, to construct estimators that learn statistically efficiently. We analyze our estimators and prove that they are more efficient than the estimators that do not use the structure of the click model, under the assumption that the click model holds. We evaluate our estimators in a series of experiments on a real-world dataset and show that they consistently outperform prior estimators.