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
Package details 


Author  Gianluca Bontempi, Catharina Olsen, Maxime Flauder 
Date of publication  20150121 00:23:55 
Maintainer  Catharina Olsen <colsen@ulb.ac.be> 
License  Artistic2.0 
Version  1.2.1 
Package repository  View on CRAN 
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