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I have read this paper with great interest, trying to understand what makes regression analysts seek the wisdom of causal diagrams when they are not asking causal questions and labor merely to assess the magnitude of measurement errors.
The answer seems to be two fold.
I have read this paper with great interest, trying to understand what makes regression analysts seek the wisdom of causal diagrams when they are not asking causal questions and labor merely to assess the magnitude of measurement errors.
The answer seems to be two fold.
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(1) The diagram allows them to use Wright's Rules ucla.in/2LcpmHz to compute correlations among latent variables (X,Y) in terms of correlations among observed proxies (x',Y'). This could be done, of course, w/o the diagram, but only at the cost of painful algebraic
(1) The diagram allows them to use Wright's Rules ucla.in/2LcpmHz to compute correlations among latent variables (X,Y) in terms of correlations among observed proxies (x',Y'). This could be done, of course, w/o the diagram, but only at the cost of painful algebraic
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derivations, as in econ. (2) The problem is in fact causal in disguise. Why else would anyone be interested in cov(X,Y) as opposed to cov(X',Y') which is
estimable from the data and is sufficient for all predictive tasks?
Curious if other readers agree. #Bookofwhy
derivations, as in econ. (2) The problem is in fact causal in disguise. Why else would anyone be interested in cov(X,Y) as opposed to cov(X',Y') which is
estimable from the data and is sufficient for all predictive tasks?
Curious if other readers agree. #Bookofwhy
