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Permutation-based causal inference algorithms with interventions
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.).
2017 (English)In: Advances in Neural Information Processing Systems, Neural information processing systems foundation , 2017, Vol. 2017, p. 5823-5832Conference paper, Published paper (Refereed)
Abstract [en]

Learning directed acyclic graphs using both observational and interventional data is now a fundamentally important problem due to recent technological developments in genomics that generate such single-cell gene expression data at a very large scale. In order to utilize this data for learning gene regulatory networks, efficient and reliable causal inference algorithms are needed that can make use of both observational and interventional data. In this paper, we present two algorithms of this type and prove that both are consistent under the faithfulness assumption. These algorithms are interventional adaptations of the Greedy SP algorithm and are the first algorithms using both observational and interventional data with consistency guarantees. Moreover, these algorithms have the advantage that they are nonparametric, which makes them useful also for analyzing non-Gaussian data. In this paper, we present these two algorithms and their consistency guarantees, and we analyze their performance on simulated data, protein signaling data, and single-cell gene expression data.

Place, publisher, year, edition, pages
Neural information processing systems foundation , 2017. Vol. 2017, p. 5823-5832
Series
Advances in Neural Information Processing Systems, ISSN 1049-5258
National Category
Bioinformatics and Systems Biology
Identifiers
URN: urn:nbn:se:kth:diva-228587Scopus ID: 2-s2.0-85046993331OAI: oai:DiVA.org:kth-228587DiVA, id: diva2:1210375
Conference
31st Annual Conference on Neural Information Processing Systems, NIPS 2017, Long Beach, United States, 4 December 2017 through 9 December 2017
Note

QC 20180528

Available from: 2018-05-28 Created: 2018-05-28 Last updated: 2018-05-28Bibliographically approved

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Solus, Liam

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  • Other locale
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