Learning causal networks from data: a survey and a new algorithm for recovering possibilistic causal networks

Causal concepts play a crucial role in many reasoning tasks. Organised as a model revealing the causal structure of a domain, they can guide inference through relevant knowledge. This is an especially difficult kind of 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 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 will review the analogies between belief networks and ''true'' causal networks and to what extent methods for learning belief networks can be used in learning causal representations. Some new results in recovering possibilistic causal networks will also be presented.