Causal Feature Selection
Isabelle Guyon, Andre Elisseeff and Constantin Aliferis
Computational Methods of Feature Selection
Data Mining and Knowledge Discovery Series
Chapman and Hall/CRC
, Boca Raton, London, New York
This chapter 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.