Learning Causal or Non-Causal Graphical Models Using Information Theory

We report an information-theoretic method which learns a large class of causal or non-causal graphical models from purely observational data, while including the effects of unobserved latent variables, commonly found in many datasets. Starting from a complete graph, the method iteratively removes dispensable edges, by uncovering significant information contributions from indirect paths, and assesses edge-specific confidences from randomization of available data. The remaining edges are then oriented based on the signature of causality in observational data. This approach can be applied on a wide range of datasets and provide new biological insights on regulatory networks from single cell expression data, genomic alterations during tumor development and co-evolving residues in protein structures. For more information you can refer to: Verny et al. Plos Comput Biol. (2017) .


Reference manual

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1.0.3 by Nadir Sella, a year ago

Browse source code at https://github.com/cran/miic

Authors: Nadir Sella [aut, cre] , Louis Verny [aut] , Severine Affeldt [aut] , Hervé Isambert [aut]

Documentation:   PDF Manual  

GPL (>= 2) license

Imports MASS, igraph, bnlearn, ppcor, stats, Rcpp

Linking to Rcpp

See at CRAN