I brought this book up some time ago. And as I read it am struck by how essential is is to understand cause and effect when working with data, analytics, machine learning and assistant services. This is MORE important than understanding code or data structure. The book does an excellent job, starting non-technically, and extending to explain recent research. It is worth at least a thoughtful scan. If you don't understand this, your modeling can be dangerously invalid. The link below goes to a free 23 page sample pdf of the book. After that, buy it for reference.
Why: A Guide to Finding and Using Causes
by Samantha Kleinberg
"Kleinberg expertly guides readers on a tour of the key concepts and methods for identifying causal relationships, with a clear and practical approach that makes Why unlike any other book on the subject. Accessible yet comprehensive, Why is essential reading for scientific novices, seasoned experts, and anyone else looking to learn more from data." .... '
Samantha Kleinberg's Site. and book ordering information.
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