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
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Being Bayesian about Network Structure
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Causal discovery from a mixture of experimental and observational data
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Learning bayesian network structure from massive datasets: the «sparse candidate« algorithm
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
An Information Geometric Perspective on Active Learning
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Active learning for Hidden Markov Models: objective functions and algorithms
ICML '05 Proceedings of the 22nd international conference on Machine learning
Journal of Biomedical Informatics
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
Active Learning for Causal Bayesian Network Structure with Non-symmetrical Entropy
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
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
Efficient and robust independence-based Markov network structure discovery
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Learning forward models for robots
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
The Journal of Machine Learning Research
Active learning of dynamic Bayesian networks in Markov decision processes
SARA'07 Proceedings of the 7th International conference on Abstraction, reformulation, and approximation
Information Systems Frontiers
Budgeted learning of nailve-bayes classifiers
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Review: learning bayesian networks: Approaches and issues
The Knowledge Engineering Review
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
Artificial Intelligence in Medicine
The Journal of Machine Learning Research
Learning linear cyclic causal models with latent variables
The Journal of Machine Learning Research
Experiment selection for causal discovery
The Journal of Machine Learning Research
Two optimal strategies for active learning of causal models from interventional data
International Journal of Approximate Reasoning
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The task of causal structure discovery from empirical data is a fundamental problem in many areas. Experimental data is crucial for accomplishing this task. However, experiments are typically expensive, and must be selected with great care. This paper uses active learning to determine the experiments that are most informative towards uncovering the underlying structure. We formalize the causal learning task as that of learning the structure of a causal Bayesian network. We consider an active learner that is allowed to conduct experiments, where it intervenes in the domain by setting the values of certain variables. We provide a theoretical framework for the active learning problem, and an algorithm that actively chooses the experiments to perform based on the model learned so far. Experimental results show that active learning can substantially reduce the number of observations required to determine the structure of a domain.