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dc.contributor.author
Bühlmann, Peter
dc.date.accessioned
2021-05-05T04:35:43Z
dc.date.available
2020-09-29T03:26:27Z
dc.date.available
2020-09-29T09:36:31Z
dc.date.available
2021-05-05T04:30:58Z
dc.date.available
2021-05-05T04:35:43Z
dc.date.issued
2020-08
dc.identifier.issn
0883-4237
dc.identifier.other
10.1214/19-STS721
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/443280
dc.identifier.doi
10.3929/ethz-b-000443280
dc.description.abstract
We discuss recent work for causal inference and predictive robustness in a unifying way. The key idea relies on a notion of probabilistic invariance or stability: it opens up new insights for formulating causality as a certain risk minimization problem with a corresponding notion of robustness. The invariance itself can be estimated from general heterogeneous or perturbation data which frequently occur with nowadays data collection. The novel methodology is potentially useful in many applications, offering more robustness and better "causal-oriented" interpretation than machine learning or estimation in standard regression or classification frameworks.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Institute of Mathematical Statistics
en_US
dc.rights.uri
http://rightsstatements.org/page/InC-NC/1.0/
dc.subject
Anchor regression
en_US
dc.subject
causal regularization
en_US
dc.subject
distributional robustness
en_US
dc.subject
heterogeneous data
en_US
dc.subject
instrumental variables regression
en_US
dc.subject
interventional data
en_US
dc.subject
Random Forests
en_US
dc.subject
variable importance
en_US
dc.title
Invariance, Causality and Robustness
en_US
dc.type
Journal Article
dc.rights.license
In Copyright - Non-Commercial Use Permitted
dc.date.published
2020-09-11
ethz.journal.title
Statistical Science
ethz.journal.volume
35
en_US
ethz.journal.issue
3
en_US
ethz.journal.abbreviated
Stat. Sci.
ethz.pages.start
404
en_US
ethz.pages.end
426
en_US
ethz.size
23 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.grant
Statistics, Prediction and Causality for Large-Scale Data
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
Cleveland, OH
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)::03502 - Bühlmann, Peter L. / Bühlmann, Peter L.
en_US
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)::03502 - Bühlmann, Peter L. / Bühlmann, Peter L.
ethz.grant.agreementno
786461
ethz.grant.fundername
EC
ethz.grant.funderDoi
10.13039/501100000780
ethz.grant.program
H2020
ethz.relation.isReferencedBy
10.3929/ethz-b-000443281
ethz.date.deposited
2020-09-29T03:26:35Z
ethz.source
WOS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2020-09-29T09:36:42Z
ethz.rosetta.lastUpdated
2022-03-29T07:02:02Z
ethz.rosetta.versionExported
true
ethz.COinS
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