Causal Language in Observational Studies: Sociocultural Backgrounds and Team Composition

TL;DR

This study analyzes how sociocultural backgrounds and team composition influence the use of causal language in observational studies, revealing that factors like author experience, gender, and country culture affect causal claims.

Contribution

It provides the first large-scale analysis linking sociocultural and team factors to causal language use in observational research.

Findings

Causal language is more common among less experienced authors.

Smaller teams tend to use more causal language.

Researchers from countries with higher uncertainty avoidance use more causal language.

Abstract

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.