It seems to me that some things that need to be known about the CR method
is how well it theoretically works. For this we have to agree that a
Pearson/Nyman truth table analog applies to this method. That is, given a
method of detecting causation (in this case, CR), what is it's type 1 and
type 2 error rates? (This ignores the possibility that the whole question
of asking to show causation with correlational data may be a type 3
error, the error of asking the wrong question.) What characteristics of
data does those error rates depend upon? Is the method actually sensitive
to uniform vs normal vs skewed, etc. distribution? Is the method
sensitive to failures of homoscedacity? etc.
Monospace that font!!
True Causality
Decision Exists Doesn't Exist
------------------------
CR Says | Correct |Type 1 Error|
Exists |Decision | |
------------------------
CR Says | Type 2 | Correct |
Not Exist| Error | Decision |
------------------------
Bill, Do you know the type 1 and type 2 error rates for your method?
Paul
.
.
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