CRAN
D2C 1.2.1
Predicting Causal Direction from Dependency Features
Released Jan 21, 2015 by Catharina Olsen
Dependencies
gRbase 1.8-3.4 randomForest 4.6-14 lazy 1.2-16 MASS 7.3-50 foreach 1.4.4 corpcor 1.6.9
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