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ChristianKl comments on Open thread, Dec. 21 - Dec. 27, 2015 - Less Wrong Discussion

2 Post author: MrMind 21 December 2015 07:56AM

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Comment author: Anders_H 23 December 2015 12:41:05AM *  2 points [-]

(1) Observational studies are almost always attempts to determine causation. Sometimes the investigators try to pretend that they aren't, but they aren't fooling anyone, least of all the general public. I know they are attempting to determine causation because nobody would be interested in the results of the study unless they were interested in causation. Moreover, I know they are attempting to determine causation because they do things like "control for confounding". This procedure is undefined unless the goal is to estimate a causal effect

(2) What do you mean by the sentence "the study was causative"? Of course nobody is suggesting that the study itself had an effect on the dependent variable?

(3) 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. The correlation is real, even if it is due to confounding. It just doesn't represent a causal effect

Comment author: FrameBenignly 23 December 2015 01:03:11AM 0 points [-]

(1) I just think calling a nonrandomized study a correlational study is weird.

(2) I meant to say effect; not study; fixed

(3) If something is caused by a confounding variable, then the independent variable may have no relationship with the dependent variable. You seem to be using correlation to mean the result of an analysis, but I'm thinking of it as the actual real relationship which is distinct from causation. So y=x does not mean y causes x or that x causes y.

Comment author: Anders_H 23 December 2015 01:18:54AM 0 points [-]

I don't understand what you mean by "real relationship". I suggest tabooing the terms "real relationship" and "no relationship".

I am using the word "correlation" to discuss whether the observed variable X predicts the observed variable Y in the (hypothetical?) superpopulation from which the sample was drawn. Such a correlation can exist even if neither variable causes the other.

If X predicts Y in the superpopulation (regardless of causality), the correlation will indeed be real. The only possible definition I can think of for a "false" correlation is one that does not exist in the superpopulation, but which appears in your sample due to sampling variability. Statistical methodology is in general more than adequate to discuss whether the appearance of correlation in your sample is due to real correlation in the superpopulation. You do not need causal inference to reason about this question. Moreover, confounding is not relevant.

Confounding and causal inference are only relevant if you want to know whether the correlation in the superpopulation is due to the causal effect of X on Y. You can certainly define the causal effect as the "actual real relationship", but then I don't understand how it is distinct from causation.