Causal attributions are claims that link an outcome to a cause (Kirfel et al., 2022). Causality is so embedded in human reasoning that causal attributions have been shown to emerge immediately in times of crisis (Graham and Singh, 2024), as well as offered spontaneously when people are asked to think about political issues (Iyengar, 1987). Furthermore, because causal attributions are relational, rather than treating actors and events as isolated, they highlight the underlying relational reasoning people use to connect events, assign responsibility, and justify actions (V ossing, 2023). Framing is fundamentally a process of making causal explanations, or communicating causal attributions: "[Frames] define problems-determine what a causal agent is doing with what costs and benefits, usually measured in terms of common cultural values; diagnose causes-identify the forces creating the problem; make moral judgments-evaluate causal agents and their effects; and suggest remedies-offer and justify treatments for the problems and predict their likely effects."(Entman,

The prevalence of drawing causal conclusions from observational studies has raised concerns about potential exaggeration in science communication. While some believe causal language should only apply to randomized controlled trials, others argue that rigorous methods can justify causal claims in observational studies. Ideally, causal language should align with the strength of the evidence. However, through the analysis of over 80,000 observational study abstracts using computational linguistic and regression methods, we found that causal language is more frequently used by less experienced authors, smaller research teams, male last authors, and authors from countries with higher uncertainty avoidance indices. These findings suggest that the use of causal language may be influenced by external factors such as the sociocultural backgrounds of authors and the dynamics of research collaboration. This newly identified link deepens our understanding of how such factors help shape scientific conclusions in causal inference and science communication.