# Unifying Online and Counterfactual Learning to Rank

arXiv: https://arxiv.org/abs/2012.04426 · HTML: https://mlsanity.com/paper/2012.04426/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Dec 2020.

## Built on by (most cited first, 15 of 15)

- [Doubly-Robust Estimation for Correcting Position-Bias in Click Feedback for Unbiased Learning to Rank](https://mlsanity.com/paper/2203.17118/) (2022)
- [Safe Deployment for Counterfactual Learning to Rank with Exposure-Based Risk Minimization](https://mlsanity.com/paper/2305.01522/) (2023)
- [Unbiased Learning to Rank Meets Reality: Lessons from Baidu's Large-Scale Search Dataset](https://mlsanity.com/paper/2404.02543/) (2024)
- [Practical and Robust Safety Guarantees for Advanced Counterfactual Learning to Rank](https://mlsanity.com/paper/2407.19943/) (2024)
- [Robust Generalization and Safe Query-Specialization in Counterfactual Learning to Rank](https://mlsanity.com/paper/2102.05990/) (2021)
- [Extending Open Bandit Pipeline to Simulate Industry Challenges](https://mlsanity.com/paper/2209.04147/) (2022)
- [Low-variance estimation in the Plackett-Luce model via quasi-Monte Carlo sampling](https://mlsanity.com/paper/2205.06024/) (2022)
- [Debias the Black-box: A Fair Ranking Framework via Knowledge Distillation](https://mlsanity.com/paper/2208.11628/) (2022)
- [Recent Advances in the Foundations and Applications of Unbiased Learning to Rank](https://mlsanity.com/paper/2305.02914/) (2023)
- [Optimizing Group-Fair Plackett-Luce Ranking Models for Relevance and Ex-Post Fairness](https://mlsanity.com/paper/2308.13242/) (2023)
- [Investigating the Robustness of Counterfactual Learning to Rank Models: A Reproducibility Study](https://mlsanity.com/paper/2404.03707/) (2024)
- [Direct Profit Estimation Using Uplift Modeling under Clustered Network Interference](https://mlsanity.com/paper/2509.01558/) (2025)
- [A Causal Information-Flow Framework for Unbiased Learning-to-Rank](https://mlsanity.com/paper/2601.05590/) (2026)
- [Accelerating A/B-Tests with Counterfactual Estimation: Reducing Variance through Policy Overlap](https://mlsanity.com/paper/2607.14604/) (2026)
- [Embedding Surgery: Localized Updates for Adaptive Ranking Correction in Dense Retrieval](https://mlsanity.com/paper/2609.05110/) (2026)

## Abstract (arXiv)

Optimizing ranking systems based on user interactions is a well-studied problem. State-of-the-art methods for optimizing ranking systems based on user interactions are divided into online approaches - that learn by directly interacting with users - and counterfactual approaches - that learn from historical interactions. Existing online methods are hindered without online interventions and thus should not be applied counterfactually. Conversely, counterfactual methods cannot directly benefit from online interventions. We propose a novel intervention-aware estimator for both counterfactual and online Learning to Rank (LTR). With the introduction of the intervention-aware estimator, we aim to bridge the online/counterfactual LTR division as it is shown to be highly effective in both online and counterfactual scenarios. The estimator corrects for the effect of position bias, trust bias, and item-selection bias by using corrections based on the behavior of the logging policy and on online interventions: changes to the logging policy made during the gathering of click data. Our experimental results, conducted in a semi-synthetic experimental setup, show that, unlike existing counterfactual LTR methods, the intervention-aware estimator can greatly benefit from online interventions.