Causal feature selection
Isabelle Guyon, Constantin Aliferis and Andre Elisseeff
Clopinet, Berkeley, California.
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.