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dc.contributor.author
Frot, Benjamin
dc.contributor.author
Nandy, Preetam
dc.contributor.author
Maathuis, Marloes H.
dc.date.accessioned
2019-06-11T10:17:49Z
dc.date.available
2019-06-10T19:22:04Z
dc.date.available
2019-06-11T10:17:49Z
dc.date.issued
2019-07
dc.identifier.issn
1369-7412
dc.identifier.issn
0035-9246
dc.identifier.issn
1467-9868
dc.identifier.other
10.1111/rssb.12315
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/346513
dc.identifier.doi
10.3929/ethz-b-000346513
dc.description.abstract
We introduce a new method to estimate the Markov equivalence class of a directed acyclic graph (DAG) in the presence of hidden variables, in settings where the underlying DAG among the observed variables is sparse, and there are a few hidden variables that have a direct effect on many of the observed variables. Building on the so‐called low rank plus sparse framework, we suggest a two‐stage approach which first removes the effect of the hidden variables and then estimates the Markov equivalence class of the underlying DAG under the assumption that there are no remaining hidden variables. This approach is consistent in certain high dimensional regimes and performs favourably when compared with the state of the art, in terms of both graphical structure recovery and total causal effect estimation.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Wiley
en_US
dc.rights.uri
http://creativecommons.org/licenses/by-nc/4.0/
dc.title
Robust causal structure learning with some hidden variables
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution-NonCommercial 4.0 International
dc.date.published
2019-03-08
ethz.journal.title
Journal of the Royal Statistical Society. Series B, Statistical Methodology
ethz.journal.volume
81
en_US
ethz.journal.issue
3
en_US
ethz.journal.abbreviated
J. R. Stat. Soc. B
ethz.pages.start
459
en_US
ethz.pages.end
487
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.scopus
ethz.publication.place
Hoboken, NJ
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02000 - Dep. Mathematik / Dep. of Mathematics::02537 - Seminar für Statistik (SfS) / Seminar for Statistics (SfS)::03789 - Maathuis, Marloes / Maathuis, Marloes
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02000 - Dep. Mathematik / Dep. of Mathematics::02537 - Seminar für Statistik (SfS) / Seminar for Statistics (SfS)::03789 - Maathuis, Marloes / Maathuis, Marloes
ethz.date.deposited
2019-06-10T19:22:12Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2019-06-11T10:18:01Z
ethz.rosetta.lastUpdated
2021-02-15T04:45:06Z
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true
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true
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