Is Information Theory Inherently a Theory of Causation?
This work addresses causal inference for researchers in statistics and machine learning, presenting a novel but incremental approach.
The paper tackles the problem of causal skeleton discovery by proposing a tensor-based method from information theory, which reduces data dimensionality and determines causal structures using pairwise tensors for three-variable systems.
Information theory gives rise to a novel method for causal skeleton discovery by expressing associations between variables as tensors. 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 pair-wise determined tensors. To arrive at this result, an additional information measure, path information, is proposed.