Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Adaptive Probabilistic Networks with Hidden Variables
Machine Learning - Special issue on learning with probabilistic representations
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Introduction to Bayesian Networks
Introduction to Bayesian Networks
A Guide to the Literature on Learning Probabilistic Networks from Data
IEEE Transactions on Knowledge and Data Engineering
Learning Belief Networks in the Presence of Missing Values and Hidden Variables
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Equivalence and synthesis of causal models
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Optimal structure identification with greedy search
The Journal of Machine Learning Research
Active learning for structure in Bayesian networks
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
A transformational characterization of equivalent Bayesian network structures
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Learning bayesian network equivalence classes from incomplete data
DS'06 Proceedings of the 9th international conference on Discovery Science
Learning causal bayesian networks from observations and experiments: a decision theoretic approach
MDAI'06 Proceedings of the Third international conference on Modeling Decisions for Artificial Intelligence
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
Review: learning bayesian networks: Approaches and issues
The Knowledge Engineering Review
Incremental causal network construction over event streams
Information Sciences: an International Journal
| Hi-index | 0.00 |
This paper proposes a new method for learning causal Bayesian networks from incomplete observational data and interventions. We extend our Greedy Equivalence Search-Expectation Maximization (GES-EM) algorithm [2], initially proposed to learn Bayesian networks from incomplete observational data, by adding a new step allowing the discovery of correct causal relationships using interventional data. Two intervention selection approaches are proposed: an adaptive one, where interventions are done sequentially and where the impact of each intervention is considered before starting the next one, and a non-adaptive one, where the interventions are executed simultaneously. An experimental study shows the merits of the new version of the GES-EM algorithm by comparing the two selection approaches.