Who is more likely to make causal claims in observational studies? The role of author experience, team size, cultural background, and gender in scientific framing

Description

Jun Wang and Bei Yu analysed more than ninety thousand observational study abstracts to learn who reaches for causal language. Observational data cannot prove cause and effect, so strong causal wording should track evidence strength alone. Their 2026 study used language analysis and regression to test whether author traits predict causal phrasing instead.

Causal language appeared more often in work by less experienced authors. Smaller teams used it more, as did papers with male last authors. Authors from cultures scoring high on uncertainty avoidance, a preference for certainty over ambiguity, also leaned causal. Wang and Yu argue certainty in print is partly social, and editors may need to police causal claims beyond the data's strength.

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Source: https://doi.org/10.1371/journal.pone.0354292