and i thought fitness/"f" was a clear example where we could do stats w/o modeling and not really learn much
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in the book, you mention thomson then say g "exists" in any event; i think existence claims require models
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unless it's the case that any factor i get out of PCA etc "exists," giving us a weird ontology full of...
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...things with no clear tie to the casual structure we're more sure of (from physics etc)
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Replying to @nostalgebraist @slatestarcodex
I think we actually agree in every respect. By "exists" I just meant that the factor is there (it could've been otherwise, cf. Thurstone)...
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...in the data, explains a lot of the variance (again, could've been otherwise), and predicts stuff IRL.
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Replying to @StuartJRitchie @slatestarcodex
*nod* but if those are the only criteria, we'd end up granting existence to nonsense variables ...
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...like "IQ plus cube root of height" if they turned out to correlate w irl stuff (as that one likely would)
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and i think the "not a nonsense variable" criterion is hiding some casual theorizing on our part
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correlation between a "nonsense var" and (say) income would be uninteresting bc we'd know it wasn't causal
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so when we grant "existence" we're saying "this seems like a good candidate for a node in a causal model"
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if we go from pure descriptive stats to "this exists," we've smuggled in some causal claims under the radar
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