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


Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.


1.2.1 by Catharina Olsen, 4 years ago

Browse source code at

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