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dc.contributor.author
Gamella, Juan L.
dc.contributor.author
Heinze-Deml, Christina
dc.contributor.editor
Larochelle, Hugo
dc.contributor.editor
Ranzato, Marc'Aurelio
dc.contributor.editor
Hadsell, Raia
dc.contributor.editor
Balcan, Maria F.
dc.contributor.editor
Lin, H.
dc.date.accessioned
2021-07-21T07:15:02Z
dc.date.available
2021-07-15T10:43:29Z
dc.date.available
2021-07-21T06:50:15Z
dc.date.available
2021-07-21T07:15:02Z
dc.date.issued
2021
dc.identifier.isbn
978-1-7138-2954-6
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/495074
dc.description.abstract
A fundamental difficulty of causal learning is that causal models can generally not be fully identified based on observational data only. Interventional data, that is, data originating from different experimental environments, improves identifiability. However, the improvement depends critically on the target and nature of the interventions carried out in each experiment. Since in real applications experiments tend to be costly, there is a need to perform the right interventions such that as few as possible are required. In this work we propose a new active learning (i.e. experiment selection) framework (A-ICP) based on Invariant Causal Prediction (ICP) [27]. For general structural causal models, we characterize the effect of interventions on so-called stable sets, a notion introduced by [30]. We leverage these results to propose several intervention selection policies for A-ICP which quickly reveal the direct causes of a response variable in the causal graph while maintaining the error control inherent in ICP. Empirically, we analyze the performance of the proposed policies in both population and finite-regime experiments.
en_US
dc.language.iso
en
en_US
dc.publisher
Curran
en_US
dc.title
Active Invariant Causal Prediction: Experiment Selection through Stability
en_US
dc.type
Conference Paper
dc.date.published
2020
ethz.book.title
Advances in Neural Information Processing Systems 33
en_US
ethz.pages.start
15464
en_US
ethz.pages.end
15475
en_US
ethz.event
34th Annual Conference on Neural Information Processing Systems (NeurIPS 2020)
en_US
ethz.event.location
Online
en_US
ethz.event.date
December 6-12, 2020
en_US
ethz.notes
Due to the Coronavirus (COVID-19) the conference was conducted virtually.
en_US
ethz.identifier.wos
ethz.publication.place
Red Hook, NY
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.identifier.url
https://proceedings.neurips.cc/paper/2020/hash/b197ffdef2ddc3308584dce7afa3661b-Abstract.html
ethz.relation.isNewVersionOf
20.500.11850/466689
ethz.date.deposited
2021-07-15T10:44:36Z
ethz.source
WOS
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2021-07-21T06:50:23Z
ethz.rosetta.lastUpdated
2022-03-29T10:33:21Z
ethz.rosetta.versionExported
true
ethz.COinS
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