Is Information Theory Inherently a Theory of Causation?

This tensor-based approach reduces the dimensionality of the data needed to test for conditional independence, e.g., for systems comprising three variables, the causal skeleton can be determined using pairwise determined tensors. To arrive at this result, an additional information measure, path information, is proposed. The gold standard for causal inference is experimentation. of information that channel can transfer, the so-called Deliberately changing one variable while channel capacity [8], equals zero, no direct causal relation keeping all other variables constant, tests for three can exist between the input and output of the channel, necessary conditions of a causal association: temporal and the edge is not shown in the graph. Using an additional precedence of the cause over the effect, the existence of measure of association, path-based mutual information a physical influence, and finally, the distinction between or path information in short, we show that for a an apparent direct association, and a "real" direct system comprising three variables, pairwise determined association [1]. When experiments, or interventions, are measures can differentiate between direct and indirect not possible, other methods are needed to test whether associations.