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.

Scientific communication relies on language that conveys different levels of certainty about research findings. In observational studies, causal language-which attributes cause-and-effect relationships-is an important way researchers express certainty, as they must balance confidence in their findings against the limitations of observational data. Ideally, the use of causal language should depend only on the strength of the underlying evidence. However, through the analysis of over 90,000 abstracts from observational studies using computational linguistic and regression methods, we found that causal language is more common in work by less experienced authors, smaller research teams, male last authors, and researchers from countries with higher uncertainty avoidance indices-a cultural dimension reflecting a society's preference for certainty over ambiguity. Our findings suggest that the use of causal language is not solely driven by the strength of evidence, but also by the sociocultural backgrounds of authors and their team composition. This work provides a new perspective for understanding systematic patterns in how scientists express certainty, emphasizing the importance of recognizing these human factors when evaluating scientific claims.