Design of Randomized Experiments in Networks.
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| Title: | Design of Randomized Experiments in Networks. |
|---|---|
| Authors: | Walker, Dylan, Muchnik, Lev |
| Source: | Proceedings of the IEEE. Dec2014, Vol. 102 Issue 12, p1940-1951. 12p. |
| Subjects: | Big data, Cybernetics research, Psychological research, Randomized controlled trials, Human-computer interaction |
| Abstract: | Over the last decade, the emergence of pervasive online and digitally enabled environments has created a rich source of detailed data on human behavior. Yet, the promise of big data has recently come under fire for its inability to separate correlation from causation—to derive actionable insights and yield effective policies. Fortunately, the same online platforms on which we interact on a day-to-day basis permit experimentation at large scales, ushering in a new movement toward big experiments. Randomized controlled trials are the heart of the scientific method and when designed correctly provide clean causal inferences that are robust and reproducible. However, the realization that our world is highly connected and that behavioral and economic outcomes at the individual and population level depend upon this connectivity challenges the very principles of experimental design. The proper design and analysis of experiments in networks is, therefore, critically important. In this work, we categorize and review the emerging strategies to design and analyze experiments in networks and discuss their strengths and weaknesses. [ABSTRACT FROM PUBLISHER] |
| Copyright of Proceedings of the IEEE 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.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 100025549 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Design of Randomized Experiments in Networks. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Walker%2C+Dylan%22">Walker, Dylan</searchLink><br /><searchLink fieldCode="AR" term="%22Muchnik%2C+Lev%22">Muchnik, Lev</searchLink> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Proceedings+of+the+IEEE%22">Proceedings of the IEEE</searchLink>. Dec2014, Vol. 102 Issue 12, p1940-1951. 12p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Big+data%22">Big data</searchLink><br /><searchLink fieldCode="DE" term="%22Cybernetics+research%22">Cybernetics research</searchLink><br /><searchLink fieldCode="DE" term="%22Psychological+research%22">Psychological research</searchLink><br /><searchLink fieldCode="DE" term="%22Randomized+controlled+trials%22">Randomized controlled trials</searchLink><br /><searchLink fieldCode="DE" term="%22Human-computer+interaction%22">Human-computer interaction</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Over the last decade, the emergence of pervasive online and digitally enabled environments has created a rich source of detailed data on human behavior. Yet, the promise of big data has recently come under fire for its inability to separate correlation from causation—to derive actionable insights and yield effective policies. Fortunately, the same online platforms on which we interact on a day-to-day basis permit experimentation at large scales, ushering in a new movement toward big experiments. Randomized controlled trials are the heart of the scientific method and when designed correctly provide clean causal inferences that are robust and reproducible. However, the realization that our world is highly connected and that behavioral and economic outcomes at the individual and population level depend upon this connectivity challenges the very principles of experimental design. The proper design and analysis of experiments in networks is, therefore, critically important. In this work, we categorize and review the emerging strategies to design and analyze experiments in networks and discuss their strengths and weaknesses. [ABSTRACT FROM PUBLISHER] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Proceedings of the IEEE 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.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=100025549 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1109/JPROC.2014.2363674 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 1940 Subjects: – SubjectFull: Big data Type: general – SubjectFull: Cybernetics research Type: general – SubjectFull: Psychological research Type: general – SubjectFull: Randomized controlled trials Type: general – SubjectFull: Human-computer interaction Type: general Titles: – TitleFull: Design of Randomized Experiments in Networks. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Walker, Dylan – PersonEntity: Name: NameFull: Muchnik, Lev IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2014 Type: published Y: 2014 Identifiers: – Type: issn-print Value: 00189219 Numbering: – Type: volume Value: 102 – Type: issue Value: 12 Titles: – TitleFull: Proceedings of the IEEE Type: main |
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