Association between author metadata and acceptance: A feature-rich, matched observational study of a corpus of ICLR submissions between 2017-2022

29 Nov 2022  ·  Chang Chen, Jiayao Zhang, Dan Roth, Ting Ye, Bo Zhang ·

Many recent studies have probed status bias in the peer-review process of academic journals and conferences. In this article, we investigated the association between author metadata and area chairs' final decisions (Accept/Reject) using our compiled database of 5,313 borderline submissions to the International Conference on Learning Representations (ICLR) from 2017 to 2022. We carefully defined elements in a cause-and-effect analysis, including the treatment and its timing, pre-treatment variables, potential outcomes and causal null hypothesis of interest, all in the context of study units being textual data and under Neyman and Rubin's potential outcomes (PO) framework. We found some weak evidence that author metadata was associated with articles' final decisions. We also found that, under an additional stability assumption, borderline articles from high-ranking institutions (top-30% or top-20%) were less favored by area chairs compared to their matched counterparts. The results were consistent in two different matched designs (odds ratio = 0.82 [95% CI: 0.67 to 1.00] in a first design and 0.83 [95% CI: 0.64 to 1.07] in a strengthened design). We discussed how to interpret these results in the context of multiple interactions between a study unit and different agents (reviewers and area chairs) in the peer-review system.

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