Computer Science professor Samantha Kleinberg's first book Causality, Probability, and Time has just been published by Cambridge University Press. 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 remain an open problem. In particular, the timing and complexity of relationships have 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. Dr. Kleinberg's 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 approach allows one to infer a relationship such as “smoking and asbestos exposure until a particular genetic mutation occurs causes lung cancer with probability 0.6 in between 1 and 3 years” without any prior knowledge of a connection between these variables or its timing. In addition to this type of type-level finding, the book introduces methods for explanation with incomplete and uncertain information, building on type-level knowledge while allowing that individual cases may deviate from this. The book contains both theoretical and experimental case studies, and the datasets used in it have been made publicly available.