Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Active learning for structure in Bayesian networks
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Causal discovery from a mixture of experimental and observational data
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Causal Graphical Models with Latent Variables: Learning and Inference
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Learning Causal Bayesian Networks from Incomplete Observational Data and Interventions
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Scalable pattern mining with Bayesian networks as background knowledge
Data Mining and Knowledge Discovery
Integrating Ontological Knowledge for Iterative Causal Discovery and Visualization
ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
The Journal of Machine Learning Research
SemCaDo: a serendipitous strategy for learning causal Bayesian networks using ontologies
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
Review: learning bayesian networks: Approaches and issues
The Knowledge Engineering Review
Incremental causal network construction over event streams
Information Sciences: an International Journal
Two optimal strategies for active learning of causal models from interventional data
International Journal of Approximate Reasoning
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We discuss a decision theoretic approach to learn causal Bayesian networks from observational data and experiments. We use the information of observational data to learn a completed partially directed acyclic graph using a structure learning technique and try to discover the directions of the remaining edges by means of experiment. We will show that our approach allows to learn a causal Bayesian network optimally with relation to a number of decision criteria. Our method allows the possibility to assign costs to each experiment and each measurement. We introduce an algorithm that allows to actively add results of experiments so that arcs can be directed during learning. A numerical example is given as demonstration of the techniques.