Causal Language in Observational Studies: Sociocultural Backgrounds and Team Composition
This research highlights sociocultural and team factors affecting scientific communication, offering insights for evaluating claims in observational studies, though it is incremental in building on existing concerns about causal language.
The study analyzed over 90,000 abstracts from observational studies to investigate factors influencing the use of causal language, finding that it is more common among less experienced authors, smaller teams, male last authors, and researchers from countries with higher uncertainty avoidance indices.
The use of causal language in observational studies has raised concerns about overstatement in scientific communication. While some argue that such language should be reserved for randomized controlled trials, others contend that rigorous causal inference methods can justify causal claims in observational research. Ideally, causal language should align with 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 are more common in work by less experienced authors, smaller research teams, male last authors, and researchers from countries with higher uncertainty avoidance indices. 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 variations in scientific communication and emphasizes the importance of recognizing these human factors when evaluating scientific claims.