D2C: Predicting Causal Direction from Dependency Features

The relationship between statistical dependency and causality lies at the heart of all statistical approaches to causal inference. The D2C package implements a supervised machine learning approach to infer the existence of a directed causal link between two variables in multivariate settings with n>2 variables. The approach relies on the asymmetry of some conditional (in)dependence relations between the members of the Markov blankets of two variables causally connected. The D2C algorithm predicts the existence of a direct causal link between two variables in a multivariate setting by (i) creating a set of of features of the relationship based on asymmetric descriptors of the multivariate dependency and (ii) using a classifier to learn a mapping between the features and the presence of a causal link

Version: 1.2.1
Depends: R (≥ 2.10.0), randomForest
Imports: gRbase, lazy, RBGL, MASS, corpcor, methods, Rgraphviz, foreach
Suggests: knitr
Published: 2015-01-21
Author: Gianluca Bontempi, Catharina Olsen, Maxime Flauder
Maintainer: Catharina Olsen <colsen at ulb.ac.be>
License: Artistic-2.0
NeedsCompilation: no
CRAN checks: D2C results


Reference manual: D2C.pdf
Vignettes: D2C Vignette
Package source: D2C_1.2.1.tar.gz
Windows binaries: r-devel: D2C_1.2.1.zip, r-release: D2C_1.2.1.zip, r-oldrel: D2C_1.2.1.zip
OS X binaries: r-release: D2C_1.2.1.tgz, r-oldrel: D2C_1.2.1.tgz
Old sources: D2C archive


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