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
Learning Bayesian networks from data: an information-theory based approach
Artificial Intelligence
A Linear Non-Gaussian Acyclic Model for Causal Discovery
The Journal of Machine Learning Research
Proceedings of the 25th international conference on Machine learning
A partial correlation-based algorithm for causal structure discovery with continuous variables
IDA'07 Proceedings of the 7th international conference on Intelligent data analysis
A heuristic partial-correlation-based algorithm for causal relationship discovery on continuous data
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
MIDAS - an influence diagram for management of mildew in winter wheat
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Learning Causal Relations in Multivariate Time Series Data
ACM Transactions on Intelligent Systems and Technology (TIST)
Learning bayesian networks from Markov random fields: An efficient algorithm for linear models
ACM Transactions on Knowledge Discovery from Data (TKDD)
Transforming graph data for statistical relational learning
Journal of Artificial Intelligence Research
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Bayesian network learning algorithms have been widely used for causal discovery since the pioneer work [13,18]. Among all existing algorithms, three-phase dependency analysis algorithm (TPDA) [5] is the most efficient one in the sense that it has polynomial-time complexity. However, there are still some limitations to be improved. First, TPDA depends on mutual information-based conditional independence (CI) tests, and so is not easy to be applied to continuous data. In addition, TPDA uses two phases to get approximate skeletons of Bayesian networks, which is not efficient in practice. In this paper, we propose a two-phase algorithm with partial correlation-based CI tests: the first phase of the algorithm constructs a Markov random field from data, which provides a close approximation to the structure of the true Bayesian network; at the second phase, the algorithm removes redundant edges according to CI tests to get the true Bayesian network. We show that two-phase algorithm with partial correlation-based CI tests can deal with continuous data following arbitrary distributions rather than only Gaussian distribution.