Causality, Probability, and Time

by Samantha Kleinberg

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.

Genres: Philosophy, Mathematics

270 pages, ebook

First published November 1, 2012

Reviews

[Dalton TM · Rating 3 out of 5]

Kleinberg’s novel measure of causality is intuitive, and she provides several real-world applications of her causal measure, but she might be understating the difficulty of defining causal processes in practice. She says transition probabilities from one event to another can be estimated through repeated observations; however this may be infeasible for many processes that rarely repeat, hindering the applicability of her measure. There is a chapter about token (single occurrence) causality, but her method for estimating token causality relies on knowing the general causal relationships and probabilities.