2020/10/05 by David Sigtermans, Sigtermans, David
Computer Science · Mathematics · #FOS: Computer and information sciences #Information Theory (cs.IT) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #cs.IT #cs.LG #math.IT #stat.ML
paper · pdf · doi:10.48550/arxiv.2010.01932
4 pages, 1 figure
arxiv created 2020/11/07 · arxiv updated 2020/11/10
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.