A Guide to Finding and Using Causes
Can drinking coffee help people live longer? What makes a stock's price go up? Why did you get the flu? You contend with questions like these on a regular basis, but it's unlikely you ever took a course on how to find causes or examined the process you use to make these judgments.
Whether you want to use your running logs to figure out why you get injured, or be able to evaluate scientific claims more critically, Why will help you sharpen your existing causal inference skills and develop new ones.
This book is a broad overview that doesn't assume any background knowledge and is suitable for anyone with an interest in causality.
Causality, Probability, and Time
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.
This book is more technical and better suited to researchers, grad students, and those with a mathematical background.