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In observational studies, causal language\u2014which attributes cause-and-effect relationships\u2014is 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\u2014a cultural dimension reflecting a society\u2019s 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. 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