Correlation-based approaches to causal analysis contain too much irrelevant information that masks and modulates the true nature of causal processes in the world. Both causal modeling and path analysis/structural equations give the wrong answers for certain conceptions of causation, given certain assumptions about the "error" variables. An alternative approach, the conditional probability approach (CP), uses conditional probability and not correlation as the key concept. The CP approach can avoid the shortcomings and problems of such methods as causal modeling and path analysis. It provides plausible composition and decomposition rules as well as a plausible measure of causal strength. Presented in the supplement are CP theorems which cover both dichotomous and continuous cases under two sets of assumptions about the "outside causes" of a system which involves probabilistic causation. (Author/PN)