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AstraSequi comments on Results of a One-Year Longitudinal Study of CFAR Alumni - Less Wrong
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Comments (35)
You'll have to clarify those points. For the first part, M-bias is not confounding. It's a kind of selection bias, and it happens when there is no causal relation with the independent or dependent variables (not no correlation), specifically when you try to adjust for confounding that doesn't exist. The collider can be a confounder, but it doesn't have to be. From the second link, "some authors refer to this type of (M-bias) as confounding...but this extension has no practical consequences"
I don't think you can get a good control group after the fact, because you need their outcomes at both timepoints, with a year in between. None of the options that come to mind are very good: you could ask them what they would have answered a year ago, you could start collecting data now and ask them in a year's time, or you could throw out the temporal data and use only a single cross-section.
Yes, M-bias is an example of a situation where a variable depends on treatment and outcome, but is not a confounder. Hence I was confused by your statement:
Confounding is not about that at all.
I used "depends" informally, so I didn't mean to say that variables that depend on treatment and outcome are always confounders. I was answering the implication that a variable with no detectable correlation with the outcome is not likely to be a source of confounding. I assumed they were using a correlational definition of confounding, so I answered in that context.
Should be careful with that, might confuse people, see also:
https://en.wikipedia.org/wiki/Confounding
which gets it wrong.
A variable with no detectable correlation with the outcome might still be a confounder, of course, you might have unfaithful things going on, or dependence might be non-linear. "Unlikely" usually implies "with respect to some model" you have in mind. How do you know that model is right? What if the true model is highly unfaithful for some reason? etc. etc.
edit: I don't mean to jump on you specifically, but it sort of is unfortunate that it somehow is a social norm to say wrong things in statistics "informally." To me, that's sort of like saying "don't worry, when I said 2+2=5, I was being informal."
Very true. This is something I'll try to change.
Cheers! If you know what M-bias is, we must have hung out in similar circles. Where did you learn "the causal view of epi"?
If Wikipedia get's it wrong it might be high leverage to correct it.
We talked about this before. I disagree with wikipedia's philosophy, and don't have time to police edits there. Wikipedia doesn't have a process in place to recognize that the opinion of someone like me on a subject like confounding is worth considerably more than the opinion of a randomly sampled internet person. I like *overflow a lot better.
One somewhat subtle point in that article is that it is titled "confounding" (which is easy to define), but then tries to define "a confounder" which is much harder, and might not be a well-defined concept according to some people.