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

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

Research output: Contribution to journalArticleResearchpeer-review

13 Citations (Scopus)

Abstract

We estimated the degree to which language used in the high-profile medical/public health/epidemiology literature implied causality using language linking exposures to outcomes and action recommendations; examined disconnects between language and recommendations; identified the most common linking phrases; and estimated how strongly linking phrases imply causality. We searched for and screened 1,170 articles from 18 high-profile journals (65 per journal) published from 2010–2019. Based on written framing and systematic guidance, 3 reviewers rated the degree of causality implied in abstracts and full text for exposure/outcome linking language and action recommendations. Reviewers rated the causal implication of exposure/outcome linking language as none (no causal implication) in 13.8%, weak in 34.2%, moderate in 33.2%, and strong in 18.7% of abstracts. The implied causality of action recommendations was higher than the implied causality of linking sentences for 44.5% or commensurate for 40.3% of articles. The most common linking word in abstracts was “associate” (45.7%). Reviewers’ ratings of linking word roots were highly heterogeneous; over half of reviewers rated “association” as having at least some causal implication. This research undercuts the assumption that avoiding “causal” words leads to clarity of interpretation in medical research.

Original languageEnglish
Pages (from-to)2084-2097
Number of pages14
JournalAmerican Journal of Epidemiology
Volume191
Issue number12
DOIs
Publication statusPublished - 1 Dec 2022
Externally publishedYes

Keywords

  • association
  • causal inference
  • causal language
  • observational study

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