Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Equivalence and synthesis of causal models
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Strong completeness and faithfulness in Bayesian networks
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Statistical inference and data mining
Communications of the ACM
Statistical Themes and Lessons for Data Mining
Data Mining and Knowledge Discovery
A Guide to the Literature on Learning Probabilistic Networks from Data
IEEE Transactions on Knowledge and Data Engineering
CMSB '03 Proceedings of the First International Workshop on Computational Methods in Systems Biology
Challenge: what is the impact of Bayesian networks on learning?
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
Learning Bayesian network equivalence classes with Ant Colony optimization
Journal of Artificial Intelligence Research
Ancestor relations in the presence of unobserved variables
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
A Bayesian method for causal modeling and discovery under selection
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Methodological Review: A review of causal inference for biomedical informatics
Journal of Biomedical Informatics
Review: learning bayesian networks: Approaches and issues
The Knowledge Engineering Review
Hi-index | 0.02 |
We show that there is a general, informative and reliable procedure for discovering causal relations when, for all the investigator knows, both latent variables and selection bias may be at work. Given information about conditional independence and dependence relations between measured variables, even when latent variables and selection bias may be present, there are sufficient conditions for reliably concluding that there is a causal path from one variable to another, and sufficient conditions for reliably concluding when no such causal path exists.