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
|Author||Gianluca Bontempi, Catharina Olsen, Maxime Flauder|
|Date of publication||2015-01-21 00:23:55|
|Maintainer||Catharina Olsen <email@example.com>|
alarm: Alarm dataset
BER: Balanced Error Rate
compute-DAG.network-method: compute N samples according to the network distribution
D2C-class: An S4 class to store the RF model trained on the basis of the...
DAG.network-class: An S4 class to store DAG.network
dataset: Dataset of the Alarm benchmark
descriptor: compute descriptor
example: stored D2C object
initialize-D2C.descriptor-method: creation of a D2C.descriptor
initialize-D2C-method: creation of a D2C object which preprocesses the list of DAGs...
initialize-DAG.network-method: creation of a DAG.network
initialize-simulatedDAG-method: creation of a "simulatedDAG" containing a list of DAGs and...
mimr: mIMR (minimum Interaction max Relevance) filter
predict-D2C-method: predict if there is a connection between node i and node j
simulatedDAG-class: An S4 class to store a list of DAGs and associated...
true.net: Adjacency matrix of the Alarm dataset
updateD2C-D2C-method: update of a "D2C" with a list of DAGs and associated...
update-simulatedDAG-method: update of a "simulatedDAG" with a list of DAGs and associated...