Reading daily doses of WMBriggs.com linked to increases in manliness.
Forwarding posts from WMBriggs.com associated with general all-around magnificence.
Supporting or donating to WMBriggs.com increases world benevolence levels.
Now these are all instances of causal language: linked to, associated with, increases. We have seen these, and others like them, used endless times in peer-reviewed papers making the most preposterous and idiotic claims.
And we have warned you against such language, by proving the many ways that cause cannot be had from probability models, especially the fast-and-loose probability models often used in medicine.
Time and again you have heard our joke, Every scientist knows correlation is not causation, unless his P is wee, then his correlation becomes causation. As far as jokes go, it does not rate high on the hilarity scale. But what it says is true.
Finally we have some counting confirmation in the peer-reviewed paper “Causal and Associational Language in Observational Health Research: A systematic evaluation” by Noah Haber and a shotgun load of others in the American Journal of Epidemiology.
This team did the tedious, mind-numbing, but useful work of tallying abuses of causal language in medical papers.
Before we get to it, a small reminder that in statistics “significant” means “having a wee P”, and where “having a wee P” means “significant”: the statistical word “significant” has no connection with the English word of the same spelling and pronunciation. Our authors found that “significant(ly)” was the most used modifier word for results.
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.
They then classified and weighted words by perceived causal association, which is surely error prone to some extent, but not by much, because it’s hard to argue with these words being causal. For instance, we see there was great agreement in raters in saying “consistent” and “correlated” had low causal-language strength, and that words like “cause” (of course) and “prevent” had high causal-language strength.
Their list, I think, most would agree with. Here’s the count:
I would have guessed “link(ed)” would be higher on the list, but “associate(d)” is just as weaselly. Say “death is associated with exposure” and you seem to have described a cause: exposure caused death. But, later, when it is shown that exposure and death were yet another spurious correlation, the researcher can say “We only said ‘associated.’ We didn’t claim cause.”
Unless the exposure is to something sexy, like third-hand smoke or MAGAism or “whiteness” or whatever, and the “research” generates a headline in some media source. Then the author, glad of being interviewed, will lather on causal words like, well, like a reporter showering praise on a celebrity.
Papers surveyed based on experiments used causal language more often than papers based on observations. This, of course, is not crazy, because the point of experiments is, or should be, to control at least some known causes. It’s rare, though, as regular readers know, that cause has been sufficiently proved in many papers.
In case you thought it was just me out on the lawn yelling at clouds, a clip from their Conclusion (my paragraphifications):
Our results suggest that “Schrödinger’s causal inference,” – where studies avoid stating (or even explicitly deny) an interest in estimating causal effects yet are otherwise embedded with causal intent, inference, implications, and recommendations – is common in the observational health literature.
While the relative paucity of explicit action recommendations might be seen as appropriate caution, it also invites causal inference since there are often no useful and/or obvious alternative (non-causal) interpretations. To our surprise, we found that the RCTs in our sample used similar linking words to the non-RCTs.
Our review suggests that the degree of causal interpretation for common linking words has been impacted by the unavailability of explicitly causal language, such that the meaning of traditionally non-causal words has broadened to include potentially stronger causal interpretations. It is likely that the rhetorical standard of “just say association” has meant that many researchers no longer fully believe that the word “association” just means association.
I hadn’t heard the lovely term “Schrödinger’s causal inference,” before. It comes from a 2021 note by Peter W G Tennant and Eleanor J Murray titled “The Quest for Timely Insights into COVID-19 Should not Come at the Cost of Scientific Rigor” in Epidemiology.
“Schrodinger’s inferences”, as above, are “where the authors caution against causal interpretations while themselves offering causal interpretations”.
Terrific. I’m going to use that.
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