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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Functions

alarm Man page
BER Man page
compute,DAG.network-method Man page
D2C-class Man page
DAG.network-class Man page
dataset Man page
descriptor Man page
example Man page
initialize,D2C.descriptor-method Man page
initialize,D2C-method Man page
initialize,DAG.network-method Man page
initialize,simulatedDAG-method Man page
mimr Man page
predict,D2C-method Man page
simulatedDAG-class Man page
true.net Man page
updateD2C,D2C-method Man page
update,simulatedDAG-method Man page

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