D2C-class


An S4 class to store the RF model trained on the basis of the descriptors of NDAG DAGs


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

D2C-class: An S4 class to store the RF model trained on the basis of the descriptors of NDAG DAGs

Description

Arguments