causality, probability, and time book cover Causality, Probability, and Time
Samantha Kleinberg, Cambridge University Press, (November 2012)
Available at: [Cambridge] [Amazon] [Kindle] []
Now also available in paperback: [Cambridge (20% discount with code KLEINBERG2018)]

"This new book on causality is a wonderful combination surveying past work and moving on to develop useful new concepts such as probabilistic temporal logic to give new definitions and results about the nature of causes. Formal theorems and practical case studies about causality are given equally detailed attention."
Patrick Suppes, Stanford University

"This book presents an exciting new approach to causality based on temporal logic. Kleinberg does an excellent job in integrating a thorough understanding of present-day philosophical approaches to causality with formal and computational considerations, to deliver an approach that is both well motivated and practically oriented. It is recommended to those interested in theories of causality as well as those concerned with the practice of causal inference."
Jon Williamson, University of Kent

"...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 [full review]

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.

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Table of contents


The simulated financial time series data used in chapter 7 are available for use in research.


The slides and syllabus from my causal inference course have been posted. If you'd like the original powerpoint files to use these slides in your (academic, noncommercial) presentations or teaching, email me at and I'd be happy to send them to you.


Page 28: In the equation for Ki, c_3 should be x_3
Page 77: Both references to definition 2.3.2 (in the theorem and text above) should be to definition 2.3.1
Page 225: Theorem B.2.1 should refer to definition 2.3.1 (not 2.3.2 as shown)

Please let me know if you find errors!