https://mw.hh.se/caisr/index.php?action=history&feed=atom&title=Publications%3AIncremental_causal_discovery_and_visualization 2026-04-04T21:36:40Z Revision history for this page on the wiki MediaWiki 1.35.13 https://mw.hh.se/caisr/index.php?title=Publications:Incremental_causal_discovery_and_visualization&diff=4516&oldid=prev 2020-02-04T21:22:28Z

<p>Created page with &quot;&lt;div style=&#039;display: none&#039;&gt; == Do not edit this section == &lt;/div&gt; {{PublicationSetupTemplate|Author=Anders Holst, Sepideh Pashami, Juhee Bae |PID=1391292 |Name=Holst, Anders (...&quot;</p> <p><b>New page</b></p><div>&lt;div style=&#039;display: none&#039;&gt;<br /> == Do not edit this section ==<br /> &lt;/div&gt;<br /> {{PublicationSetupTemplate|Author=Anders Holst, Sepideh Pashami, Juhee Bae<br /> |PID=1391292<br /> |Name=Holst, Anders (RISE SICS, Stockholm, Sweden);Pashami, Sepideh (seppas) (0000-0003-3272-4145) (Högskolan i Halmstad (2804), Akademin för informationsteknologi (16904), Halmstad Embedded and Intelligent Systems Research (EIS) (3938), CAISR Centrum för tillämpade intelligenta system (IS-lab) (13650));Bae, Juhee (School of Informatics, University of Skövde, Sweden)<br /> |Title=Incremental causal discovery and visualization<br /> |PublicationType=Conference Paper<br /> |ContentType=Refereegranskat<br /> |Language=eng<br /> |Journal=<br /> |JournalISSN=<br /> |Status=<br /> |Volume=<br /> |Issue=<br /> |HostPublication=Proceedings of the Workshop on Interactive Data Mining, WIDM 2019<br /> |Conference=1st Workshop on Interactive Data Mining, WIDM 2019, co-located with 12th ACM International Conference on Web Search and Data Mining, WSDM 2019, 15 February 2019<br /> |StartPage=<br /> |EndPage=<br /> |Year=2019<br /> |Edition=<br /> |Pages=<br /> |City=<br /> |Publisher=Association for Computing Machinery, Inc<br /> |Series=<br /> |SeriesISSN=<br /> |ISBN=9781450362962<br /> |Urls=<br /> |ISRN=<br /> |DOI=http://dx.doi.org/10.1145/3304079.3310287<br /> |ISI=<br /> |PMID=<br /> |ScopusId=2-s2.0-85069768142<br /> |NBN=urn:nbn:se:hh:diva-41536<br /> |LocalId=<br /> |ArchiveNumber=<br /> |Keywords=Causal Discovery;Correlation;Incremental Visualization;Correlation methods;Data mining;Visualization;Causal graph;Causal relations;Discovery algorithm;Incremental discoveries;Novel visualizations;Data visualization<br /> |Categories=Sannolikhetsteori och statistik (10106);Mediateknik (21102);Datavetenskap (datalogi) (10201)<br /> |ResearchSubjects=<br /> |Projects=<br /> |Notes=<br /> |Abstract=&lt;p&gt;Discovering causal relations from limited amounts of data can be useful for many applications. However, all causal discovery algorithms need huge amounts of data to estimate the underlying causal graph. To alleviate this gap, this paper proposes a novel visualization tool which incrementally discovers causal relations as more data becomes available. That is, we assume that stronger causal links will be detected quickly and weaker links revealed when enough data is available. In addition to causal links, the correlation between variables and the uncertainty of the strength of causal links are visualized in the same graph. The tool is illustrated on three example causal graphs, and results show that incremental discovery works and that the causal structure converges as more data becomes available. © 2019 Copyright held by the owner/author(s). Publication rights licensed to ACM.&lt;/p&gt;<br /> |Opponents=<br /> |Supervisors=<br /> |Examiners=<br /> |Patent=<br /> |ThesisLevel=<br /> |Credits=<br /> |Programme=<br /> |Subject=<br /> |Uppsok=<br /> |DefencePlace=<br /> |DefenceLanguage=<br /> |DefenceDate=<br /> |CreatedDate=2020-02-04<br /> |PublicationDate=2020-02-04<br /> |LastUpdated=2020-02-04<br /> |diva=http://hh.diva-portal.org/smash/record.jsf?searchId=1&amp;pid=diva2:1391292}}<br /> &lt;div style=&#039;display: none&#039;&gt;<br /> <br /> == Keep all hand-made modifications below ==<br /> &lt;/div&gt;{{PublicationDisplayTemplate}}</div>

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