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Visual Analytics Methodologies on Causality Analysis

Abstract Causality 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 relation... (more)
Created Date 2019
Contributor Wang, Hong Xiang (Author) / Maciejewski, Ross (Advisor) / He, Jingrui (Committee member) / Davulcu, Hasan (Committee member) / Thies, Cameron (Committee member) / Arizona State University (Publisher)
Subject Computer science / Causality / Visual Analytics
Type Doctoral Dissertation
Extent 138 pages
Language English
Note Doctoral Dissertation Computer Science 2019
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis