Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A Bayesian method for constructing Bayesian belief networks from databases
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Probabilistic Expert Systems
Equivalence and synthesis of causal models
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
A transformational characterization of equivalent Bayesian network structures
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
An Empirical Study of Encoding Schemes and Search Strategies in Discovering Causal Networks
ECML '02 Proceedings of the 13th European Conference on Machine Learning
An Improved Approach for the Discovery of Causal Models via MML
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Data mining tasks and methods: Probabilistic and casual networks: mining for probabilistic networks
Handbook of data mining and knowledge discovery
Introduction: machine learning as philosophy of science
Minds and Machines - Machine learning as experimental philosophy of science
Incorporating expert knowledge when learning Bayesian network structure: A medical case study
Artificial Intelligence in Medicine
Study of ensemble strategies in discovering linear causal models
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
Causal discovery of dynamic bayesian networks
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
Formal-Transfer In and Out of Stroke Care Units: An Analysis Using Bayesian Networks
International Journal of Healthcare Information Systems and Informatics
Hi-index | 0.00 |
Weak causal relationships and small sample size pose two significant difficulties to the automatic discovery of causal models from observational data. This paper examines the influence of weak causal links and varying sample sizes on the discovery of causal models. The experimental results illustrate the effect of larger sample sizes for discovering causal models reliably and the relevance of the strength of causal links and the complexity of the original causal model. We present indicative evidence of the superior robustness of MML (Minimum Message Length) methods to standard significance tests in the recovery of causal links. The comparative results show that the MML-CI (the MML Causal Inducer) causal discovery system finds better models than TETRAD II given small samples from linear causal models. The experimental results also reveal that MML-CI finds weak links with smaller sample sizes than can TETRAD II.