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


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install.packages("D2C")

1.2.1 by Catharina Olsen, 4 years ago


Browse source code at https://github.com/cran/D2C


Authors: Gianluca Bontempi , Catharina Olsen , Maxime Flauder


Documentation:   PDF Manual  


Artistic-2.0 license


Imports gRbase, lazy, RBGL, MASS, corpcor, methods, Rgraphviz, foreach

Depends on randomForest

Suggests knitr


See at CRAN