dataset


contains the  measured dataset. See the vignette for an utilization of the dataset


data

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

dataset: Dataset of the Alarm benchmark

Description

Arguments

Details

References