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dc.contributor.author
Hatt, Tobias
dc.contributor.author
Feuerriegel, Stefan
dc.date.accessioned
2021-12-02T09:43:32Z
dc.date.available
2021-12-02T09:43:32Z
dc.date.issued
2021-10
dc.identifier.isbn
978-1-4503-8446-9
en_US
dc.identifier.other
10.1145/3459637.3482339
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/518159
dc.description.abstract
Decision-making often requires accurate estimation of treatment effects from observational data. This is challenging as outcomes of alternative decisions are not observed and have to be estimated. Previous methods estimate outcomes based on unconfoundedness but neglect any constraints that unconfoundedness imposes on the outcomes. In this paper, we propose a novel regularization framework for estimating average treatment effects that exploits unconfoundedness. To this end, we formalize unconfoundedness as an orthogonality constraint, which ensures that the outcomes are orthogonal to the treatment assignment. This orthogonality constraint is then included in the loss function via a regularization. Based on our regularization framework, we develop deep orthogonal networks for unconfounded treatments (DONUT), which learn outcomes that are orthogonal to the treatment assignment. Using a variety of benchmark datasets for estimating average treatment effects, we demonstrate that DONUT outperforms the state-of-the-art substantially. © 2021 ACM
en_US
dc.language.iso
en
en_US
dc.publisher
Association for Computing Machinery
dc.subject
Causal Inference
en_US
dc.subject
Treatment Effect Estimation
en_US
dc.subject
Regularization
en_US
dc.subject
Neural Networks
en_US
dc.title
Estimating Average Treatment Effects via Orthogonal Regularization
en_US
dc.type
Conference Paper
dc.date.published
2021-10-26
ethz.book.title
Proceedings of the 30th ACM International Conference on Information & Knowledge Management
en_US
ethz.pages.start
680
en_US
ethz.pages.end
689
en_US
ethz.event
30th ACM International Conference on Information and Knowledge Management (CIKM '21)
en_US
ethz.event.location
Online
ethz.event.date
November 1-5, 2021
en_US
ethz.identifier.scopus
ethz.publication.place
New York, NY
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02120 - Dep. Management, Technologie und Ökon. / Dep. of Management, Technology, and Ec.::09623 - Feuerriegel, Stefan (ehemalig) / Feuerriegel, Stefan (former)
en_US
ethz.date.deposited
2021-08-09T17:18:28Z
ethz.source
SCOPUS
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2021-12-02T09:43:40Z
ethz.rosetta.lastUpdated
2024-02-02T15:28:11Z
ethz.rosetta.versionExported
true
dc.identifier.olduri
http://hdl.handle.net/20.500.11850/516417
dc.identifier.olduri
http://hdl.handle.net/20.500.11850/500174
ethz.COinS
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