miic: Learning Causal or Non-Causal Graphical Models Using Information Theory

Multivariate Information-based Inductive Causation, better known by its acronym MIIC, is a causal discovery method, based on information theory principles, which learns a large class of causal or non-causal graphical models from purely observational data, while including the effects of unobserved latent variables. 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. The recent more interpretable MIIC extension (iMIIC) further distinguishes genuine causes from putative and latent causal effects, while scaling to very large datasets (hundreds of thousands of samples). Since the version 2.0, MIIC also includes a temporal mode (tMIIC) to learn temporal causal graphs from stationary time series data. MIIC has been applied to a wide range of biological and biomedical data, such as single cell gene expression data, genomic alterations in tumors, live-cell time-lapse imaging data (CausalXtract), as well as medical records of patients. MIIC brings unique insights based on causal interpretation and could be used in a broad range of other data science domains (technology, climatology, economy, ...). For more information, you can refer to: Simon et al., eLife 2024, <doi:10.1101/2024.02.06.579177>, Ribeiro-Dantas et al., iScience 2024, <doi:10.1016/j.isci.2024.109736>, Cabeli et al., NeurIPS 2021, <https://why21.causalai.net/papers/WHY21_24.pdf>, Cabeli et al., Comput. Biol. 2020, <doi:10.1371/journal.pcbi.1007866>, Li et al., NeurIPS 2019, <https://papers.nips.cc/paper/9573-constraint-based-causal-structure-learning-with-consistent-separating-sets>, Verny et al., PLoS Comput. Biol. 2017, <doi:10.1371/journal.pcbi.1005662>, Affeldt et al., UAI 2015, <https://auai.org/uai2015/proceedings/papers/293.pdf>. Changes from the previous 1.5.3 release on CRAN are available at <https://github.com/miicTeam/miic_R_package/blob/master/NEWS.md>.

Version:	2.0.3
Imports:	ppcor, Rcpp, scales, stats
LinkingTo:	Rcpp
Suggests:	igraph, grDevices, ggplot2 (≥ 3.3.0), gridExtra
Published:	2024-09-17
DOI:	10.32614/CRAN.package.miic
Author:	Franck Simon [aut, cre], Tiziana Tocci [aut], Nikita Lagrange [aut], Orianne Debeaupuis [aut], Louise Dupuis [aut], Vincent Cabeli [aut], Honghao Li [aut], Marcel Ribeiro Dantas [aut], Nadir Sella [aut], Louis Verny [aut], Severine Affeldt [aut], Hervé Isambert [aut]
Maintainer:	Franck Simon <franck.simon at curie.fr>
BugReports:	https://github.com/miicTeam/miic_R_package/issues
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL:	https://github.com/miicTeam/miic_R_package
NeedsCompilation:	yes
In views:	Omics
CRAN checks:	miic results

Documentation:

Reference manual:

miic.pdf

Downloads:

Package source:	miic_2.0.3.tar.gz
Windows binaries:	r-devel: miic_2.0.3.zip, r-release: miic_2.0.3.zip, r-oldrel: miic_2.0.3.zip
macOS binaries:	r-release (arm64): miic_2.0.3.tgz, r-oldrel (arm64): miic_2.0.3.tgz, r-release (x86_64): miic_2.0.3.tgz, r-oldrel (x86_64): miic_2.0.3.tgz
Old sources:	miic archive

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