Causal and Associational Language in Observational Health Research: A Systematic Evaluation

Autor: Cathrine Axfors, Arthur Chatton, Elizabeth A. Stuart, Ariadne E. Rivera Aguirre, Julia M. Rohrer, Ian Schmid, Palwasha Khan, Daloha Rodríguez-Molina, Sebastián Peña, Sophie Pilleron, Camila Olarte Parra, Mark Kelson, Saman Khalatbari-Soltani, Jessie Seiler, Mi-Suk Kang Dufour, Eleanor J Murray, Peter W. G. Tennant, Anna Booman, Meg G. Salvia, Daniel J. Dunleavy, Taym M. Alsalti, Thomas Rhys Evans, Philipp Schoenegger, Rachel A. Hoopsick, Sarah Wieten, Sze Tung Lam, Gideon Meyerowitz-Katz, Stefanie Do, Rebekah Baglini, Sarah E. Twardowski, Sarah J Howcutt, Matthew P. Fox, Mari Takashima, Onyebuchi A. Arah, Julia Dabravolskaj, Clemence Leyrat, Emily Riederer, Shashank Suresh, Ashley L. O’Donoghue, Alberto Antonietti, Noah Haber, Eric Au, Nnaemeka U. Odo, Taylor McLinden, José Andrés Calvache, Alison E. Simmons, Talal S. Alshihayb, Nicholas Judd, Andreea Steriu

Publikováno v: Haber, N, Wieten, S, Rohrer, J, Arah, O, Tennant, P, Stuart, E, Murray, E, Pilleron, S, Lam, S T, Riederer, E, Howcutt, S J, Simmons, A, Leyrat, C, Schoenegger, P, Booman, A, Dufour, M-S K, O'Donoghue, A & Baglini, R B 2022, ' Causal and associational language in observational health research: A systematic evaluation ', American Journal of Epidemiology . https://doi.org/10.1101/2021.08.25.21262631

Background Avoiding “causal” language with observational study designs is common publication practice, often justified as being a more cautious approach to interpretation. Objectives We aimed to i) estimate the degree to which causality was impli

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