An explanatory theory. Not 'data'.
My explanation of Judea Pearl's causal calculus: "If correlation doesn't imply causation, then what does?" http://www.michaelnielsen.org/ddi/if-correlation-doesnt-imply-causation-then-what-does/ …https://twitter.com/hardmaru/status/997987139726921728 …
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Implicitly, there's some of this in the causal calculus - explanatory theories must be where the underlying graphs come from. But it'd be nice to put these two points of view properly together.
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The Neyman-Rubin framework is a lot like Pearl's, except that it's specialized to the setting of "Here are N binary random variables that all affect my variable of interest."
End of conversation
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