Operations Research
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Theory refinement on Bayesian networks
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Equivalence and synthesis of causal models
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Operations for learning with graphical models
Journal of Artificial Intelligence Research
Bayesian Networks for Data Mining
Data Mining and Knowledge Discovery
Scalable Techniques for Mining Causal Structures
Data Mining and Knowledge Discovery
Scalable Techniques for Mining Causal Structures
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
The Evolution of Causal Models: A Comparison of Bayesian Metrics and Structure Priors
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
Multimodal metadata fusion using causal strength
Proceedings of the 13th annual ACM international conference on Multimedia
Journal of Biomedical Informatics
A study of causal discovery with weak links and small samples
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Decision-theoretic foundations for causal reasoning
Journal of Artificial Intelligence Research
Challenge: what is the impact of Bayesian networks on learning?
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
The Knowledge Engineering Review
Causal discovery from a mixture of experimental and observational data
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Asymptotic model selection for directed networks with hidden variables*
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
A graph-theoretic analysis of information value
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Expert Systems with Applications: An International Journal
Computational Statistics & Data Analysis
Artificial Intelligence in Medicine
Incremental causal network construction over event streams
Information Sciences: an International Journal
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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 parameter independence, parameter modularity, and likelihood equivalence. We show that although these assumptions also can be appropriate for learning causal networks, we need additional assumptions in order to learn causal networks. We introduce two sufficient assumptions, called mechanism independence and component independence. We show that these new assumptions, when combined with parameter independence, parameter modularity, and likelihood equivalence, allow us to apply methods for learning acausal networks to learn causal networks.