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

Getting started

Package details

AuthorGianluca Bontempi, Catharina Olsen, Maxime Flauder
MaintainerCatharina Olsen <colsen@ulb.ac.be>
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:

Try the D2C package in your browser

Any scripts or data that you put into this service are public.

D2C documentation built on May 29, 2017, 10:44 a.m.