Description
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 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.
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Details
Title
- Visual analytics methodologies on causality analysis
Contributors
- Wang, Hong, Ph.D (Author)
- Maciejewski, Ross (Thesis advisor)
- He, Jingrui (Committee member)
- Davulcu, Hasan (Committee member)
- Thies, Cameron (Committee member)
- Arizona State University (Publisher)
Date Created
The date the item was original created (prior to any relationship with the ASU Digital Repositories.)
2019
Subjects
Resource Type
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Note
- thesisPartial requirement for: Ph.D., Arizona State University, 2019
- bibliographyIncludes bibliographical references
- Field of study: Computer science
Citation and reuse
Statement of Responsibility
by Hong Wang