You are assuming a couple of things which are almost always true in your (medical) field, but are not necessarily true in general. For example,

Observational studies are almost always attempts to determine causation

Nope. Another very common reason is to create a predictive model without caring about actual causation. If you can't do interventions but would like to forecast the future, that's all you need.

Assuming that the statistics were done correctly and that the investigators have accounted for sampling variability, the relationship between the independent and dependent variable definitely exists.

That further assumes your underlying process is stable and is not subject to drift, regime changes, etc. Sometimes you can make that assumption, sometimes you cannot.

Another very common reason is to create a predictive model without caring about actual causation. If you can't do interventions but would like to forecast the future, that's all you need.

You'd also like a guarantee that others can't do interventions, or else your measure could be gamed. (But if there's an actual causal relationship, then 'gaming' isn't really possible.)

Open thread, Dec. 21 - Dec. 27, 2015

by MrMind 1 min read21st Dec 2015233 comments

2


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