PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Causal feature selection
Isabelle Guyon, Constantin Aliferis and Andre Elisseeff
(2007) Technical Report. Clopinet, Berkeley, California.

Abstract

This report reviews techniques for learning causal relationships from data, in application to the problem of feature selection. Most feature selection methods do not attempt to uncover causal relationships between feature and target and focus instead on making best predictions. We examine situations in which the knowledge of causal relationships benefits feature selection. Such benefits may include: explaining relevance in terms of causal mechanisms, distinguishing between actual features and experimental artifacts, predicting the consequences of actions performed by external agents, and making predictions in non-stationary environments. Conversely, we highlight the benefits that causal discovery may draw from recent developments in feature selection theory and algorithms.

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EPrint Type:Monograph (Technical Report)
Additional Information:A shorter version of this TM will appear as a book chapter in “Computational Methods of Feature Selection”, Huan Liu and Hiroshi Motoda Eds., by Chapman and Hall/CRC Press, 2007.
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:2977
Deposited By:Isabelle Guyon
Deposited On:03 April 2007