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Provincial versus Open Science Epidemiology

A characteristic of provincialism is to coin jargon terms with vocabulary that is not so useful in the open science 21st century with need for effective cross-discipline communication.

One example is “Mendelian randomization” instead of the more cosmopolitan “instrumental variable” term as originally created with economics origins.

Other examples are ‘collider bias’ and ‘backdoor paths’ and ‘reverse causality,’ with the latter having a perfectly good and sensible meaning in theoretical physics before it was grabbed by provincial epidemiology and re-appropriated when other terms such as cyclic or feedback loop might have been chosen.


Isn’t it time to re-frame the epidemiology vocabulary (e.g., for DAGs thinking), and become explicit advocates for 21st century open science and cross-disciplinary communication as opposed to the current provincialism as now practiced in epidemiology?


Let’s come up with terms that have more cross-discipline communication value, replacing our provincial ‘collider bias’ and ‘backdoor paths’ with terms that will have more lasting value.


What do you think, Prof Hernan?



Great idea. But more difficult than it appears.

Words have meaning but this differs for different people. Something as simple as "bias" means something different to statisticians, epidemiologists, and non-scientists. I recently went through the exercise of trying to find an alternative term for collider bias. We failed. I don't like Miguel Hernan's use of "per protocol effect" because I think it is too easily confused with "per protocol analysis".  But when he challenged me to come up with a better term, I couldn't.

So I would suggest you start us off by creating the list of words you consider as jargon, and then write down the words you think we should use. I think it is very challenging to both precise, and unambiguous across different cultures.