  Despite peer-reviewing being an essential component of academia since the
1600s, it has repeatedly received criticisms for lack of transparency and
consistency. We posit that recent work in machine learning and explainable AI
provide tools that enable insights into the decisions from a given peer review
process. We start by extracting global explanations in the form of linguistic
features that affect the acceptance of a scientific paper for publication on an
open peer-review dataset. Second, since such global explanations do not justify
causal interpretations, we provide a methodology for detecting confounding
effects in natural language in order to generate causal explanations, under
assumptions, in the form of lexicons. Our proposed linguistic explanation
methodology indicates the following on a case dataset of ICLR submissions: a)
the organising committee follows, for the most part, the recommendations of
reviewers, and, b) the paper's main characteristics that led to reviewers
recommending acceptance for publication are originality, clarity and substance.
