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?
Download count: 0
- Partial requirement for: Ph.D., Arizona State University, 2021
- Field of study: Computer Engineering