Academic Journal
A Visual Analytics Approach for Exploratory Causal Analysis: Exploration, Validation, and Applications.
| Τίτλος: | A Visual Analytics Approach for Exploratory Causal Analysis: Exploration, Validation, and Applications. |
|---|---|
| Συγγραφείς: | Xie, Xiao, Du, Fan, Wu, Yingcai |
| Πηγή: | IEEE Transactions on Visualization & Computer Graphics; Feb2021, Vol. 27 Issue 2, p1448-1458, 11p |
| Θεματικοί όροι: | Visual analytics, Social sciences education, Decision making, Workflow management, Education marketing, Social medicine, Visualization |
| Περίληψη: | Using causal relations to guide decision making has become an essential analytical task across various domains, from marketing and medicine to education and social science. While powerful statistical models have been developed for inferring causal relations from data, domain practitioners still lack effective visual interface for interpreting the causal relations and applying them in their decision-making process. Through interview studies with domain experts, we characterize their current decision-making workflows, challenges, and needs. Through an iterative design process, we developed a visualization tool that allows analysts to explore, validate, and apply causal relations in real-world decision-making scenarios. The tool provides an uncertainty-aware causal graph visualization for presenting a large set of causal relations inferred from high-dimensional data. On top of the causal graph, it supports a set of intuitive user controls for performing what-if analyses and making action plans. We report on two case studies in marketing and student advising to demonstrate that users can effectively explore causal relations and design action plans for reaching their goals. [ABSTRACT FROM AUTHOR] |
| Copyright of IEEE Transactions on Visualization & Computer Graphics is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Links: – Type: other Text: Availability: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Visual Analytics Approach for Exploratory Causal Analysis: Exploration, Validation, and Applications. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Xie%2C+Xiao%22">Xie, Xiao</searchLink><br /><searchLink fieldCode="AR" term="%22Du%2C+Fan%22">Du, Fan</searchLink><br /><searchLink fieldCode="AR" term="%22Wu%2C+Yingcai%22">Wu, Yingcai</searchLink> – Name: TitleSource Label: Source Group: Src Data: IEEE Transactions on Visualization & Computer Graphics; Feb2021, Vol. 27 Issue 2, p1448-1458, 11p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Visual+analytics%22">Visual analytics</searchLink><br /><searchLink fieldCode="DE" term="%22Social+sciences+education%22">Social sciences education</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+making%22">Decision making</searchLink><br /><searchLink fieldCode="DE" term="%22Workflow+management%22">Workflow management</searchLink><br /><searchLink fieldCode="DE" term="%22Education+marketing%22">Education marketing</searchLink><br /><searchLink fieldCode="DE" term="%22Social+medicine%22">Social medicine</searchLink><br /><searchLink fieldCode="DE" term="%22Visualization%22">Visualization</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Using causal relations to guide decision making has become an essential analytical task across various domains, from marketing and medicine to education and social science. While powerful statistical models have been developed for inferring causal relations from data, domain practitioners still lack effective visual interface for interpreting the causal relations and applying them in their decision-making process. Through interview studies with domain experts, we characterize their current decision-making workflows, challenges, and needs. Through an iterative design process, we developed a visualization tool that allows analysts to explore, validate, and apply causal relations in real-world decision-making scenarios. The tool provides an uncertainty-aware causal graph visualization for presenting a large set of causal relations inferred from high-dimensional data. On top of the causal graph, it supports a set of intuitive user controls for performing what-if analyses and making action plans. We report on two case studies in marketing and student advising to demonstrate that users can effectively explore causal relations and design action plans for reaching their goals. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of IEEE Transactions on Visualization & Computer Graphics is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1109/TVCG.2020.3028957 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 1448 Subjects: – SubjectFull: Visual analytics Type: general – SubjectFull: Social sciences education Type: general – SubjectFull: Decision making Type: general – SubjectFull: Workflow management Type: general – SubjectFull: Education marketing Type: general – SubjectFull: Social medicine Type: general – SubjectFull: Visualization Type: general Titles: – TitleFull: A Visual Analytics Approach for Exploratory Causal Analysis: Exploration, Validation, and Applications. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Xie, Xiao – PersonEntity: Name: NameFull: Du, Fan – PersonEntity: Name: NameFull: Wu, Yingcai IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2021 Type: published Y: 2021 Identifiers: – Type: issn-print Value: 10772626 Numbering: – Type: volume Value: 27 – Type: issue Value: 2 Titles: – TitleFull: IEEE Transactions on Visualization & Computer Graphics Type: main |
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