Chapter title |
Discovery of Causal Models that Contain Latent Variables Through Bayesian Scoring of Independence Constraints
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Chapter number | 9 |
Book title |
Machine Learning and Knowledge Discovery in Databases
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Published in |
Machine learning and knowledge discovery in databases : European Conference, ECML PKDD ... : proceedings. ECML PKDD (Conference), September 2017
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DOI | 10.1007/978-3-319-71246-8_9 |
Pubmed ID | |
Book ISBNs |
978-3-31-971245-1, 978-3-31-971246-8
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Authors |
Fattaneh Jabbari, Joseph Ramsey, Peter Spirtes, Gregory Cooper |
Abstract |
Discovering causal structure from observational data in the presence of latent variables remains an active research area. Constraint-based causal discovery algorithms are relatively efficient at discovering such causal models from data using independence tests. Typically, however, they derive and output only one such model. In contrast, Bayesian methods can generate and probabilistically score multiple models, outputting the most probable one; however, they are often computationally infeasible to apply when modeling latent variables. We introduce a hybrid method that derives a Bayesian probability that the set of independence tests associated with a given causal model are jointly correct. Using this constraint-based scoring method, we are able to score multiple causal models, which possibly contain latent variables, and output the most probable one. The structure-discovery performance of the proposed method is compared to an existing constraint-based method (RFCI) using data generated from several previously published Bayesian networks. The structural Hamming distances of the output models improved when using the proposed method compared to RFCI, especially for small sample sizes. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 24 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 11 | 46% |
Researcher | 3 | 13% |
Student > Master | 2 | 8% |
Student > Bachelor | 1 | 4% |
Other | 1 | 4% |
Other | 1 | 4% |
Unknown | 5 | 21% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 12 | 50% |
Engineering | 2 | 8% |
Biochemistry, Genetics and Molecular Biology | 1 | 4% |
Physics and Astronomy | 1 | 4% |
Mathematics | 1 | 4% |
Other | 2 | 8% |
Unknown | 5 | 21% |