Isabelle Guyon, Constantin Aliferis, André Elisseeff.
To appear in “Computational Methods of Feature Selection”, Huan Liu and Hiroshi Motoda Eds., scheduled to be published in the mid 2007 by Chapman and Hall/CRC Press.
We review 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.