There are two facts which make correlations controlling for confounds uninformative about many (but not all) causal effects: 1) the R^2 of the mechanisms we understand is low, 2) our uncertainty about not well-understood mechanisms should be high. (1 / about 13-15)
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Now, given confounds you listed (and assumptions about their correlations -- do you know those?), you can simulate how much uncertainty you should have about due to the confounds you enumerated. It will likely be far larger than any plausible range of true effects.
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But guess what? That almost surely understates the uncertainty you should have. After all, even if you could somehow control for all those things we enumerated that we can't control for, your R^2 would still be low! Most of the mechanisms determining mortality are still unknown!
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Chances are, you've enumerated only a small fraction of the possible confounds. If you could somehow enumerate all of those, you would see that in a Bayesian sense, the variation in estimates from confounding factors will dwarve any plausible variation in direct effect sizes.
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In other words, inference by controlling for confounds is really hard unless you have a research design that circumscribes the set of possible confounds at the outset, or an unusual setting where the R^2 is well-understood (e.g. a treatment to prevent acute mortality)
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I should mention there are methods that can be used in observational data that formalize related ideas: https://brown.edu/research/projects/oster/sites/brown.edu.research.projects.oster/files/uploads/Unobservable_Selection_and_Coefficient_Stability_0.pdf …. These methods correctly conclude that adding controls with little impact on R^2 teach you little.
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