Is Information Theory Inherently a Theory of Causation?

TL;DR

This paper introduces a tensor-based method for causal discovery using information theory, reducing data requirements and incorporating a new measure called path information to identify causal structures.

Contribution

It presents a novel tensor-based approach for causal skeleton discovery that leverages a new information measure, path information, to improve efficiency and accuracy.

Findings

Tensor-based causal discovery reduces data dimensionality.

Path information enhances causal inference accuracy.

Method applicable to systems with multiple variables.

Abstract

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.