Causal Interpretations of Random Variables

A causal interpretation of random variables corresponds to the successive generation of these random variables by a sequence of random experiments, each of which uses the results of previous experiments only. Causality graphs are introduced to describe such a causal representation. It is shown that although the order of the random variables in a causal interpretation is completely arbitrary, causality graphs are nonetheless useful in deducing independences among random variables.