This paper introduces the notion of objection-based causal networks. which resemble probabilistic causal networks ex­ cept that tht:>y are quant.ified using ob­ jections. An objection is a logical sen­ tence and denotes a condition under which a causal dependency does not exist. Objection-based causal net.works enjoy al­ most all the properties that make proba­ bilistic causal networks popular,...

Whereas acausal Bayesian networks represent probabilistic independence, causal Bayesian networks represent causal relationships. In this paper, we examine Bayesian methods for learning both types of networks. Bayesian methods for learning acausal networks are fairly well developed. These methods often employ assumptions to facilitate the construction of priors, including the assumptions of para...

In this paper we propose a causal analog to the purely observational Dynamic Bayesian Networks, which we call Dynamic Causal Networks. We provide a sound and complete algorithm for identification of Dynamic Causal Networks, namely, for computing the effect of an intervention or experiment, based on passive observations only, whenever possible. We note the existence of two types of confounder va...

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 wor...