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Visual Analytics Methodologies on Causality Analysis
Visual Analytics Methodologies on Causality Analysis
상세정보
- 자료유형
- 학위논문
- Control Number
- 0015493073
- International Standard Book Number
- 9781085691437
- Dewey Decimal Classification Number
- 004
- Main Entry-Personal Name
- Wang, Hong.
- Publication, Distribution, etc. (Imprint
- [Sl] : Arizona State University, 2019
- Publication, Distribution, etc. (Imprint
- Ann Arbor : ProQuest Dissertations & Theses, 2019
- Physical Description
- 139 p
- General Note
- Source: Dissertations Abstracts International, Volume: 81-03, Section: B.
- General Note
- Advisor: Maciejewski, Ross.
- Dissertation Note
- Thesis (Ph.D.)--Arizona State University, 2019.
- Restrictions on Access Note
- This item must not be sold to any third party vendors.
- Summary, Etc.
- 요약Causality analysis is the process of identifying cause-effect relationships among variables. This process is challenging because causal relationships cannot be tested solely based on statistical indicators as additional information is always needed to reduce the ambiguity caused by factors beyond those covered by the statistical test. Traditionally, controlled experiments are carried out to identify causal relationships, but recently there is a growing interest in causality analysis with observational data due to the increasing availability of data and tools. This type of analysis will often involve automatic algorithms that extract causal relations from large amounts of data and rely on expert judgment to scrutinize and verify the relations. Over-reliance on these automatic algorithms is dangerous because models trained on observational data are susceptible to bias that can be difficult to spot even with expert oversight. Visualization has proven to be effective at bridging the gap between human experts and statistical models by enabling an interactive exploration and manipulation of the data and models. This thesis develops a visual analytics framework to support the interaction between human experts and automatic models in causality analysis. Three case studies were conducted to demonstrate the application of the visual analytics framework in which feature engineering, insight generation, correlation analysis, and causality inspections were showcased.
- Subject Added Entry-Topical Term
- Computer science
- Added Entry-Corporate Name
- Arizona State University Computer Science
- Host Item Entry
- Dissertations Abstracts International. 81-03B.
- Host Item Entry
- Dissertation Abstract International
- Electronic Location and Access
- 로그인을 한후 보실 수 있는 자료입니다.
- Control Number
- joongbu:569716
MARC
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■020 ▼a9781085691437
■035 ▼a(MiAaPQ)AAI22588220
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a004
■1001 ▼aWang, Hong.
■24510▼aVisual Analytics Methodologies on Causality Analysis
■260 ▼a[Sl]▼bArizona State University▼c2019
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2019
■300 ▼a139 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 81-03, Section: B.
■500 ▼aAdvisor: Maciejewski, Ross.
■5021 ▼aThesis (Ph.D.)--Arizona State University, 2019.
■506 ▼aThis item must not be sold to any third party vendors.
■520 ▼aCausality analysis is the process of identifying cause-effect relationships among variables. This process is challenging because causal relationships cannot be tested solely based on statistical indicators as additional information is always needed to reduce the ambiguity caused by factors beyond those covered by the statistical test. Traditionally, controlled experiments are carried out to identify causal relationships, but recently there is a growing interest in causality analysis with observational data due to the increasing availability of data and tools. This type of analysis will often involve automatic algorithms that extract causal relations from large amounts of data and rely on expert judgment to scrutinize and verify the relations. Over-reliance on these automatic algorithms is dangerous because models trained on observational data are susceptible to bias that can be difficult to spot even with expert oversight. Visualization has proven to be effective at bridging the gap between human experts and statistical models by enabling an interactive exploration and manipulation of the data and models. This thesis develops a visual analytics framework to support the interaction between human experts and automatic models in causality analysis. Three case studies were conducted to demonstrate the application of the visual analytics framework in which feature engineering, insight generation, correlation analysis, and causality inspections were showcased.
■590 ▼aSchool code: 0010.
■650 4▼aComputer science
■690 ▼a0984
■71020▼aArizona State University▼bComputer Science.
■7730 ▼tDissertations Abstracts International▼g81-03B.
■773 ▼tDissertation Abstract International
■790 ▼a0010
■791 ▼aPh.D.
■792 ▼a2019
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T15493073▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
■980 ▼a202002▼f2020
