Estimating the Causal Effect of Early ArXiving on Paper Acceptance
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Abstract (arXiv)
What is the effect of releasing a preprint of a paper before it is submitted for peer review? No randomized controlled trial has been conducted, so we turn to observational data to answer this question. We use data from the ICLR conference (2018--2022) and apply methods from causal inference to estimate the effect of arXiving a paper before the reviewing period (early arXiving) on its acceptance to the conference. Adjusting for confounders such as topic, authors, and quality, we may estimate the causal effect. However, since quality is a challenging construct to estimate, we use the negative outcome control method, using paper citation count as a control variable to debias the quality confounding effect. Our results suggest that early arXiving may have a small effect on a paper's chances of acceptance. However, this effect (when existing) does not differ significantly across different groups of authors, as grouped by author citation count and institute rank. This suggests that early arXiving does not provide an advantage to any particular group.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 9 of its 43 references that are in the map. First posted on arXiv 24 Jun 2023.
Builds on
9 of 43 references.
References · 9
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
- 2020Longformer: The Long-Document Transformercited by 3,413
- 2020SPECTER: Document-level Representation Learning using Citation-informed Transformerscited by 430
- 2021Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyondcited by 178
- 2023The Semantic Scholar Open Data Platformcited by 118
- 2022Investigating Fairness Disparities in Peer Review: A Language Model Enhanced Approachcited by 23
- 2018Citation Count Analysis for Papers with Preprintscited by 3
- 2017Single versus Double Blind Reviewing at WSDM 2017cited by 2
- 2022Some Reflections on Drawing Causal Inference using Textual Data: Parallels Between Human Subjects and Organized Textscited by 2
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Citing papers per year: 2024: 3; 2025: 0; 2026: 1.
4 papers.
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- 2024Position: AI/ML Influencers Have a Place in the Academic Processcited by 3
- 2026Who Pays for Open Review? Visible Author Reputation and Its Effect on Ratingscited by 0
- 2026Who Pays for Open Review? Visible Author Reputation and Its Effect on Ratings7 Sep 2026
- 2024On Behalf of the Stakeholders: Trends in NLP Model Interpretability in the Era of LLMs27 Jul 2024
- 2024AgentReview: Exploring Peer Review Dynamics with LLM Agents18 Jun 2024
- 2024Position: AI/ML Influencers Have a Place in the Academic Process24 Jan 2024