D2C (1.0)

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

Maintainer: Catharina Olsen
Author(s): Gianluca Bontempi, Catharina Olsen, Maxime Flauder

License: Artistic-2.0

Uses: corpcor, gRbase, infotheo, lazy, MASS, randomForest, RBGL

Released almost 5 years ago.