If it's worth saying, but not worth its own post (even in Discussion), then it goes here.
Notes for future OT posters:
1. Please add the 'open_thread' tag.
2. Check if there is an active Open Thread before posting a new one. (Immediately before; refresh the list-of-threads page before posting.)
3. Open Threads should be posted in Discussion, and not Main.
4. Open Threads should start on Monday, and end on Sunday.
I'm trying to get at least a vague handle on what I can legitimately infer from what using data that might, and probably does, contain circular causation. I'm looking for statistical tools that might help me do that. Should I try Bayesian causal inference anyway, just to see what I get? Support vector machines? Markov random fields? Does the Spurious Correlations book have ideas on that? (No, it just seems to be an awesome set of correlations. Thanks, BTW.)
(Also notice that these are not just any correlations. These are the strongest correlations that pertain among a large number of variables relative to each other. I mean, I computed all possible correlations among every combination of 2 variables in hopes that the strongest I find for each variable might show something interesting.)
That's not a very well-defined goal. You are engaging in what's known as a spaghetti factory analysis: make a lot of spaghetti, throw it on the wall, pick the most interesting shapes. This doesn't tell you anything about the world.
Sure, you can start with correlations. But that's only a start. Let's say you've got a high correlation between A and B. The next questions should be: Does it make sense? Is there a plausible mechanism underlying this correlation? Is it stable in time? I... (read more)