Comparison of strategies for scalable causal discovery of latent variable models from mixed data

Modern technologies allow large, complex biomedical datasets to be collected from patient cohorts. These datasets are comprised of both continuous and categorical data (“Mixed Data”), and essential variables may be unobserved in this data due to the complex nature of biomedical phenomena. Causal inf...

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Bibliographische Detailangaben
Veröffentlicht in:Int J Data Sci Anal
Hauptverfasser: Raghu, Vineet K., Ramsey, Joseph D., Morris, Alison, Manatakis, Dimitrios V., Sprites, Peter, Chrysanthis, Panos K., Glymour, Clark, Benos, Panayiotis V.
Format: Artigo
Sprache:Inglês
Veröffentlicht: Springer International Publishing 2018
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Regular Paper
Online Zugang:https://ncbi.nlm.nih.gov/pmc/articles/PMC6096780/
https://ncbi.nlm.nih.gov/pubmed/30148202
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1007/s41060-018-0104-3
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