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Dear List Members: I am developing a family of statistical methods that allows us to infer causation from continuous linear variables: http://www.wynja.com/chambers/regression.html I have applied the methods of corresponding correlations and corresponding regressions to real data but most of my work is based on simulations, I have some general questions about how experts conduct simulations. The methods I am using assume that the causes are uniformly distributed. If we create a series of such causes, the subsequent causes become progressively normally distributed. Consider the following model: y1=x1+x2, where x1 and x2 are uniformly distributed, y2=y1+x3 y3=y2+x4 Notice that half of the causes (yn) tend to be progressively more normally distributed. As the model progresses, the distributions become thinner in the extremes, This gives more weight to midrange variables, because they are more frequently instantiated, The problem is worse when we create the dependent variables directly from series of normally distributed causes, The dependent variable that is generated from normally distributed causes tends to be determined in the extremes by either one cause or the other (disjunctive causation), The combination of two extreme values of x1 and x2 (conjunctive cause) is very rare when the causes are normal because extremes of x1 or x2 are rare, even on their own. Their combination is even more rare. The upshot is that as we go from uniform to normal distributions, the causal model in the extremes of y(n) becomes progressively disjunctive while that in the mid range of y(n) stays conjunctive, Have any of you dealt with this problem before? How do you keep causal (x) distributions uniform in sequences? Thanks, William Chambers