Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
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
Using Bayesian networks to analyze expression data
RECOMB '00 Proceedings of the fourth annual international conference on Computational molecular biology
Optimal structure identification with greedy search
The Journal of Machine Learning Research
An introduction to variable and feature selection
The Journal of Machine Learning Research
Time and sample efficient discovery of Markov blankets and direct causal relations
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
A theoretical characterization of linear SVM-based feature selection
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Consistent Feature Selection for Pattern Recognition in Polynomial Time
The Journal of Machine Learning Research
Distribution-free learning of Bayesian network structure in continuous domains
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
A partial correlation-based algorithm for causal structure discovery with continuous variables
IDA'07 Proceedings of the 7th international conference on Intelligent data analysis
Causal inference and causal explanation with background knowledge
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Scalable, efficient and correct learning of markov boundaries under the faithfulness assumption
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Causality Discovery with Additive Disturbances: An Information-Theoretical Perspective
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Impact of censoring on learning Bayesian networks in survival modelling
Artificial Intelligence in Medicine
A causal discovery algorithm using multiple regressions
Pattern Recognition Letters
Introduction to Causal Inference
The Journal of Machine Learning Research
Brain effective connectivity modeling for alzheimer's disease by sparse gaussian bayesian network
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
A partial correlation-based Bayesian network structure learning algorithm under SEM
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
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)
Learning Bayesian network structure using Markov blanket decomposition
Pattern Recognition Letters
New skeleton-based approaches for Bayesian structure learning of Bayesian networks
Applied Soft Computing
Score-based methods for learning Markov boundaries by searching in constrained spaces
Data Mining and Knowledge Discovery
Estimating building simulation parameters via Bayesian structure learning
Proceedings of the 2nd International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications
Algorithms for discovery of multiple Markov boundaries
The Journal of Machine Learning Research
Hi-index | 0.00 |
We show how a generic feature-selection algorithm returning strongly relevant variables can be turned into a causal structure-learning algorithm. We prove this under the Faithfulness assumption for the data distribution. In a causal graph, the strongly relevant variables for a node X are its parents, children, and children's parents (or spouses), also known as the Markov blanket of X. Identifying the spouses leads to the detection of the V-structure patterns and thus to causal orientations. Repeating the task for all variables yields a valid partially oriented causal graph. We first show an efficient way to identify the spouse links. We then perform several experiments in the continuous domain using the Recursive Feature Elimination feature-selection algorithm with Support Vector Regression and empirically verify the intuition of this direct (but computationally expensive) approach. Within the same framework, we then devise a fast and consistent algorithm, Total Conditioning (TC), and a variant, TCbw, with an explicit backward feature-selection heuristics, for Gaussian data. After running a series of comparative experiments on five artificial networks, we argue that Markov blanket algorithms such as TC/TCbw or Grow-Shrink scale better than the reference PC algorithm and provides higher structural accuracy.