# Cite-seeing and Reviewing: A Study on Citation Bias in Peer Review

arXiv: https://arxiv.org/abs/2203.17239 · HTML: https://mlsanity.com/paper/2203.17239/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2022.

## Built on by (most cited first, 7 of 7)

- [From Replication to Redesign: Exploring Pairwise Comparisons for LLM-Based Peer Review](https://mlsanity.com/paper/2506.11343/) (2025)
- [How do Authors' Perceptions of their Papers Compare with Co-authors' Perceptions and Peer-review Decisions?](https://mlsanity.com/paper/2211.12966/) (2022)
- [Causal Effect of Group Diversity on Redundancy and Coverage in Peer-Reviewing](https://mlsanity.com/paper/2411.11437/) (2024)
- [The Emerging AI Paper-Review Arms Race: Adversarial Co-Evolution in Scholarly Publishing](https://mlsanity.com/paper/2609.07713/) (2026)
- [Paper Quality Assessment based on Individual Wisdom Metrics from Open Peer Review](https://mlsanity.com/paper/2501.13014/) (2025)
- [Quantifying the Relevance of Youth Research Cited in the US Policy Documents](https://mlsanity.com/paper/2503.04977/) (2025)
- [Who Pays for Open Review? Visible Author Reputation and Its Effect on Ratings](https://mlsanity.com/paper/2609.11983/) (2026)

## Abstract (arXiv)

Citations play an important role in researchers' careers as a key factor in evaluation of scientific impact. Many anecdotes advice authors to exploit this fact and cite prospective reviewers to try obtaining a more positive evaluation for their submission. In this work, we investigate if such a citation bias actually exists: Does the citation of a reviewer's own work in a submission cause them to be positively biased towards the submission? In conjunction with the review process of two flagship conferences in machine learning and algorithmic economics, we execute an observational study to test for citation bias in peer review. In our analysis, we carefully account for various confounding factors such as paper quality and reviewer expertise, and apply different modeling techniques to alleviate concerns regarding the model mismatch. Overall, our analysis involves 1,314 papers and 1,717 reviewers and detects citation bias in both venues we consider. In terms of the effect size, by citing a reviewer's work, a submission has a non-trivial chance of getting a higher score from the reviewer: an expected increase in the score is approximately 0.23 on a 5-point Likert item. For reference, a one-point increase of a score by a single reviewer improves the position of a submission by 11% on average.