Lalitha Viswanath wrote: > > We are trying to find out, which of A or B cause C > i.e. We are hypothesising that C is the effect and > either A or B, not both is the cause. > (...) > I would greatly appreciate any inputs on the best > statistcal approach to tackle this problem. > I am thinking that we can find correlation > coefficients between A and C, and between B and C, but > I am not sure this answers the question. > Also we do not know whether the correlation between > them is linear or non linear. > If the causation (not the correlation) is not linear, then the correlation (which is linear, always) may not be the best indicator.
Take, as an extreme case, this: A <- (-50:50) + 100 * rnorm(101) B <- abs((-50):50) + 10 * rnorm(101) C <- A^2 / 50 + rnorm(101) cor(A, C) cor(B, C) A is obviously the "cause" of C, but B (in some cases) is better correlated to C than A to C. Alberto Monteiro ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
