Determining Causal Skeletons with Information Theory.

Modeling a causal association as arising from a communication process between cause and effect, simplifies the discovery of causal skeletons. The communication channels enabling these communication processes, are fully characterized by stochastic tensors, and therefore allow us to use linear algebra. This tensor-based approach reduces the dimensionality of the data needed to test for conditional independence, e.

A Path-Based Partial Information Decomposition.

Based on the conceptual basis of information theory, we propose a novel mutual information measure-'path-based mutual information'. This information measure results from the representation of a set of random variables as a probabilistic graphical model. The edges in this graph are modeled as discrete memoryless communication channels, that is, the underlying data is ergodic, stationary, and the Markov condition is assumed to be applicable.

Towards a Framework for Observational Causality from Time Series: When Shannon Meets Turing.

We propose a tensor based approach to infer causal structures from time series. An information theoretical analysis of () shows that results from transmission of information over a set of communication channels. Tensors are the mathematical equivalents of these multichannel causal channels.