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>.