Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A characterization of the dirichlet distribution with application to learning Bayesian networks
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
A Bayesian approach to learning causal networks
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Using Bayesian networks to analyze expression data
RECOMB '00 Proceedings of the fourth annual international conference on Computational molecular biology
CMSB '03 Proceedings of the First International Workshop on Computational Methods in Systems Biology
Structure Search and Stability Enhancement of Bayesian Networks
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Journal of Biomedical Informatics
Reconfigurable computing for learning Bayesian networks
Proceedings of the 16th international ACM/SIGDA symposium on Field programmable gate arrays
Scalable pattern mining with Bayesian networks as background knowledge
Data Mining and Knowledge Discovery
Learning Bayesian networks for discrete data
Computational Statistics & Data Analysis
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
General causal representation in the medical domain
ICCOMP'09 Proceedings of the WSEAES 13th international conference on Computers
Active learning for structure in Bayesian networks
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
The Journal of Machine Learning Research
Introduction to Causal Inference
The Journal of Machine Learning Research
Unsupervised active learning in large domains
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
A Bayesian method for causal modeling and discovery under selection
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Learning causal structures based on markov equivalence class
ALT'05 Proceedings of the 16th international conference on Algorithmic Learning Theory
Review: learning bayesian networks: Approaches and issues
The Knowledge Engineering Review
Computational Statistics & Data Analysis
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
Towards integrative causal analysis of heterogeneous data sets and studies
The Journal of Machine Learning Research
Learning from mixture of experimental data: a constraint---based approach
SETN'12 Proceedings of the 7th Hellenic conference on Artificial Intelligence: theories and applications
Identifying significant edges in graphical models of molecular networks
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
Hi-index | 0.00 |
This paper describes a Bayesian method for combining an arbitrary mixture of observational and experimental data in order to learn causal Bayesian networks. Observational data are passively observed. Experimental data, such as that produced by randomized controlled trials, result from the exoerimenter manioulatine one or more variables (tipically randomiy) and observing the states of other variables. The paper presents a Bayesian method for learning the causal structure and parameters of the underlying causal process that is generating the data, given that (1) the data contains a mixture of observational and experimental case records, and (2) the causal process is modeled as a causal Bayesian network. This learning method was applied using as input various mixtures of experimental and observational data that were generated from the ALARM causal Bayesian network. In these experiments, the absolute and relative quantities of experimental and observational data were varied systematically. For each of these training datasets, the learning method was applied to predict the causal structure and to estimate the causal parameters that exist among randomly selected pairs of nodes in ALARM that are not confounded. The paper reports how these structure predictions and parameter estimates compare with the true causal structures and parameters as given by the ALARM network.