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dc.contributor.author
Xu, Sascha
dc.contributor.author
Mian, Osman A.
dc.contributor.author
Marx, Alexander
dc.contributor.author
Vreeken, Jilles
dc.contributor.editor
Chaudhuri, Kamalika
dc.contributor.editor
Jegelka, Stefanie
dc.contributor.editor
Song, Le
dc.contributor.editor
Szepesvári, Csaba
dc.contributor.editor
Niu, Gang
dc.contributor.editor
Sabato, Sivan
dc.date.accessioned
2023-03-17T06:43:53Z
dc.date.available
2023-03-16T04:21:04Z
dc.date.available
2023-03-17T06:43:53Z
dc.date.issued
2022
dc.identifier.issn
2640-3498
dc.identifier.uri
http://hdl.handle.net/20.500.11850/603475
dc.description.abstract
We study the problem of identifying cause and effect over two univariate continuous variables X and Y from a sample of their joint distribution. Our focus lies on the setting when the variance of the noise may be dependent on the cause. We propose to partition the domain of the cause into multiple segments where the noise indeed is dependent. To this end, we minimize a scale-invariant, penalized regression score, finding the optimal partitioning using dynamic programming. We show under which conditions this allows us to identify the causal direction for the linear setting with heteroscedastic noise, for the non-linear setting with homoscedastic noise, as well as empirically confirm that these results generalize to the non-linear and heteroscedastic case. Altogether, the ability to model heteroscedasticity translates into an improved performance in telling cause from effect on a wide range of synthetic and real-world datasets.
en_US
dc.language.iso
en
en_US
dc.publisher
PMLR
en_US
dc.title
Inferring Cause and Effect in the Presence of Heteroscedastic Noise
en_US
dc.type
Conference Paper
ethz.book.title
Proceedings of the 39th International Conference on Machine Learning
en_US
ethz.journal.title
Proceedings of Machine Learning Research
ethz.journal.volume
162
en_US
ethz.pages.start
24615
en_US
ethz.pages.end
24630
en_US
ethz.event
39th International Conference on Machine Learning (ICML 2022)
en_US
ethz.event.location
Baltimore, MD, USA
en_US
ethz.event.date
July 17-23, 2022
en_US
ethz.identifier.wos
ethz.publication.place
Cambridge, MA
en_US
ethz.publication.status
published
en_US
ethz.identifier.url
https://proceedings.mlr.press/v162/xu22f.html
ethz.date.deposited
2023-03-16T04:21:10Z
ethz.source
WOS
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2023-03-17T06:43:54Z
ethz.rosetta.lastUpdated
2023-03-17T06:43:54Z
ethz.rosetta.versionExported
true
ethz.COinS
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