文章基本信息

标题：The C-Word: Scientific Euphemisms Do Not Improve Causal Inference From Observational Data
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作者：Miguel A. Hernán
期刊名称：American journal of public health
印刷版ISSN：0090-0036
出版年度：2018
卷号：108
期号：5
页码：616-619
DOI：10.2105/AJPH.2018.304337
语种：English
出版社：American Public Health Association
摘要：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. You know the story: Dear author: Your observational study cannot prove causation. Please replace all references to causal effects by references to associations. Many journal editors request authors to avoid causal language, 1 and many observational researchers, trained in a scientific environment that frowns upon causality claims, spontaneously refrain from mentioning the C-word (“causal”) in their work. As a result, “causal effect” and terms with similar meaning (“impact,” “benefit,” etc.) are routinely avoided in scientific publications that describe nonrandomized studies. Instead, we see terms like “association” and others that convey a similar meaning (“correlation,” “pattern,” etc.), or the calculatedly ambiguous “link.” The proscription against the C-word is harmful to science because causal inference is a core task of science, regardless of whether the study is randomized or nonrandomized. Without being able to make explicit references to causal effects, the goals of many observational studies can only be expressed in a roundabout way. The resulting ambiguity impedes a frank discussion about methodology because the methods used to estimate causal effects are not the same as those used to estimate associations. Confusion then ensues at the most basic levels of the scientific process and, inevitably, errors are made. We need to stop treating “causal” as a dirty word that respectable investigators do not say in public or put in print. It is true that observational studies cannot definitely prove causation, but this statement misses the point, as discussed in this commentary.