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About this product
- DescriptionCausality is a key part of many fields and facets of life, from finding the relationship between diet and disease to discovering the reason for a particular stock market crash. Despite centuries of work in philosophy and decades of computational research, automated inference and explanation remains an open problem. In particular, the timing and complexity of relationships has been largely igred even though this information is critically important for prediction, explanation and intervention. However, given the growing availability of large observational datasets including those from electronic health records and social networks, it is a practical necessity. This book presents a new approach to inference (finding relationships from a set of data) and explanation (assessing why a particular event occurred), addressing both the timing and complexity of relationships. The practical use of the method developed is illustrated through theoretical and experimental case studies, demonstrating its feasibility and success.
- Author BiographySamantha Kleinberg is Assistant Professor of Computer Science at Stevens Institute of Technology. She received a PhD in Computer Science and a BA in Computer Science and Physics from New York University.
- Author(s)Samantha Kleinberg
- PublisherCambridge University Press
- Date of Publication12/11/2012
- Place of PublicationCambridge
- Country of PublicationUnited Kingdom
- ImprintCambridge University Press
- Content Note33 b/w illus. 6 tables
- Weight490 g
- Width156 mm
- Height234 mm
- Spine16 mm
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