A theory of diagnosis from first principles
Artificial Intelligence
A formal theory of plan recognition
A formal theory of plan recognition
An algorithm for deciding if a set of observed independencies has a causal explanation
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Equivalence and synthesis of causal models
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
An introduction to variable and feature selection
The Journal of Machine Learning Research
Generating random correlation matrices based on partial correlations
Journal of Multivariate Analysis
A Linear Non-Gaussian Acyclic Model for Causal Discovery
The Journal of Machine Learning Research
Using Markov Blankets for Causal Structure Learning
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
Learning Bayesian network structure using Markov blanket decomposition
Pattern Recognition Letters
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The purpose of a constraint-based causal discovery algorithm (CDA) is to find a directed acyclic graph which is observationally equivalent to the non-interventional data. Limiting the data to follow multivariate Gaussian distribution, existing such algorithms perform conditional independence (CI) tests to compute the graph structure by comparing pairs of nodes independently. In this paper, however, we propose Multiple Search algorithm which performs CI tests on multiple pairs of nodes simultaneously. Furthermore, compared to existing CDAs, the proposed algorithm searches a smaller number of conditioning sets because it continuously removes irrelevant nodes, and generates more-reliable solutions by double-checking the graph structures. We show the effectiveness of the proposed algorithm by comparison with Grow-Shrink and Collider Set algorithms through numerical experiments based on six networks.