Chance News 104: Difference between revisions
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==Forsooth== | ==Forsooth== | ||
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== Transitivity, Correlation and Causation == | |||
Theorem 1 of the article cited by Paul Alper in the previous issue, "Is the Property of Being Positively Correlated Transitive?" [http://www.jstor.org/discover/10.2307/2685695?sid=21105654939673&uid=4&uid=2&uid=3739256&uid=3739736] (The American Statistician, Vol. 55, No. 4, November, 2001), depends on the existence of non-observed independent random variables U, V, and W which ''cause'' the correlations between X=U+V, Y=W+V, and Z=W-U to be non-transitive. An interesting question is whether this relates back to the difference between causation and correlation. | |||
The surprising answer is, no, we can get the same sort of result even in the presence of causative relationships between X, Y and Z. Here’s an example: | |||
X is N(0,1). Y=X+U where U is N(0,1) and independent of X. Z=Y-1.5*X. | |||
Submitted by Emil M Friedman | |||
==Item 2== | ==Item 2== |
Revision as of 20:01, 11 March 2015
Quotations
Forsooth
Transitivity, Correlation and Causation
Theorem 1 of the article cited by Paul Alper in the previous issue, "Is the Property of Being Positively Correlated Transitive?" [1] (The American Statistician, Vol. 55, No. 4, November, 2001), depends on the existence of non-observed independent random variables U, V, and W which cause the correlations between X=U+V, Y=W+V, and Z=W-U to be non-transitive. An interesting question is whether this relates back to the difference between causation and correlation.
The surprising answer is, no, we can get the same sort of result even in the presence of causative relationships between X, Y and Z. Here’s an example:
X is N(0,1). Y=X+U where U is N(0,1) and independent of X. Z=Y-1.5*X.
Submitted by Emil M Friedman