
The C-Word: Scientific Euphemisms Do Not Improve Causal Inference From Observational Data
Author(s) -
Miguel A. Hernán
Publication year - 2018
Publication title -
american journal of public health
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.284
H-Index - 264
eISSN - 1541-0048
pISSN - 0090-0036
DOI - 10.2105/ajph.2018.304337
Subject(s) - causal inference , observational study , ambiguity , inference , interpretation (philosophy) , causal model , psychology , task (project management) , causality (physics) , cognitive psychology , epistemology , computer science , econometrics , artificial intelligence , mathematics , statistics , philosophy , physics , management , quantum mechanics , economics , programming language
Causal inference is a core task of science. However, authors and editors often refrain from explicitly acknowledging the causal goal of research projects; they refer to causal effect estimates as associational estimates. This commentary argues that using the term "causal" is necessary to improve the quality of observational research. Specifically, being explicit about the causal objective of a study reduces ambiguity in the scientific question, errors in the data analysis, and excesses in the interpretation of the results.