Learning causal networks from data
Ramon Sangüesa I Sole · 1996
Causal concepts play a crucial role in many reasoning tasks. Organized as a model revealing the causal structure of a domain, they can guide inference through relevant knowledge. This is a specially difficult knowledge to acquire, so some methods for automating the induction of causal models from data have been put forth. Here we review those that have a DAG (Directed Acyclic Graph) representation. Most work has been done on the problem of recovering belief nets from data but some extensions are appearing that claim to exhibit a true causal semantics. We'll review the analogies between belief networks and