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Causal discovery using clusters from observational data
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0003-3272-4145
RISE SICS, Stockholm, Sweden.
School of Informatics, University of Skövde, Sweden.
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0002-7796-5201
2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Many methods have been proposed over the years for distinguishing causes from effects using observational data only, and new ones are continuously being developed – deducing causal relationships is difficult enough that we do not hope to ever get the perfect one. Instead, we progress by creating powerful heuristics, capable of capturing more and more of the hints that are present in real data.

One type of such hints, quite surprisingly rarely explicitly addressed by existing methods, is in-homogeneities in the data. Clusters are a very typical occurrence that should be taken into account, and exploited, in the process of identifying causes and effects. In this paper, we discuss the potential benefits, and explore the hints that clusters in the data can provide for causal discovery. We propose a new method, and show, using both artificial and real data, that accounting for clusters in the data leads to more accurate learning of causal structures.

Place, publisher, year, edition, pages
2018.
National Category
Other Computer and Information Science
Identifiers
URN: urn:nbn:se:hh:diva-39216OAI: oai:DiVA.org:hh-39216DiVA, id: diva2:1303420
Conference
FAIM'18 Workshop on CausalML, Stockholm, Sweden, July 15, 2018
Available from: 2019-04-09 Created: 2019-04-09 Last updated: 2019-04-11Bibliographically approved

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fulltext(3755 kB)101 downloads
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Pashami, SepidehNowaczyk, Sławomir

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf