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

AuthorGianluca Bontempi, Catharina Olsen, Maxime Flauder
Date of publication2015-01-21 00:23:55
MaintainerCatharina Olsen <colsen@ulb.ac.be>
LicenseArtistic-2.0
Version1.2.1

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