Causality, Probability, and Time

Presents a new approach to causal inference and explanation, addressing both the timing and complexity of relationships.

Samantha Kleinberg (Author)

9781107026483, Cambridge University Press

Hardback, published 12 November 2012

265 pages, 33 b/w illus. 6 tables
24.2 x 16 x 1.6 cm, 0.49 kg

'… informative and engaging … Arguably an equally valuable contribution of the book is its integration of relevant work in philosophy, computer science, and statistics.' David R. Bickel, Mathematical Reviews

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.

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.

Subject Areas: Artificial intelligence [UYQ], Philosophy of science [PDA], Probability & statistics [PBT], Philosophy: logic [HPL]