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Causality, probability, and time

By: Kleinberg, Samantha.
Material type: materialTypeLabelBookPublisher: Cambridge Cambridge University Press 2013Description: vii, 259 p. Includes bibliography and index.ISBN: 9781107686014.Subject(s): Computational complexity | Probabilistic causality | Probability logicDDC classification: 511.352 Summary: Causality 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 ignored 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. https://www.cambridge.org/gb/academic/subjects/computer-science/artificial-intelligence-and-natural-language-processing/causality-probability-and-time?format=PB
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Books Vikram Sarabhai Library
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Slot 1355 (0 Floor, East Wing) Non-fiction 511.352 K5C2 (Browse shelf) Available 200710

Table of Contents

1. Introduction
2. A brief history of causality
3. Probability, logic and probabilistic temporal logic
4. Defining causality
5. Inferring causality
6. Token causality
7. Case studies
8. Conclusion
Appendix A. A little bit of statistics
Appendix B. Proofs


Causality 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 ignored 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.

https://www.cambridge.org/gb/academic/subjects/computer-science/artificial-intelligence-and-natural-language-processing/causality-probability-and-time?format=PB

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