Description
This dissertation considers the question of how convenient access to copious networked observational data impacts our ability to learn causal knowledge. It investigates in what ways learning causality from such data is different from -- or the same as -- the traditional causal inference which often deals with small scale i.i.d. data collected from randomized controlled trials?
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Contributors
- Guo, Ruocheng (Author)
- Liu, Huan (Thesis advisor)
- Candan, K. Selcuk (Committee member)
- Xue, Guoliang (Committee member)
- Kiciman, Emre (Committee member)
- Arizona State University (Publisher)
Date Created
The date the item was original created (prior to any relationship with the ASU Digital Repositories.)
2021
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Note
- Partial requirement for: Ph.D., Arizona State University, 2021
- Field of study: Computer Engineering