Time and Causality Across the Sciences

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This book, geared toward academic researchers and graduate students, brings together research on all facets of how time and causality relate across the sciences. Time is fundamental to how we perceive and reason about causes. It lets us immediately rule out the sound of a car crash as its cause. That a cause happens before its effect has been a core, and often unquestioned, part of how we describe causality. Research across disciplines shows that the relationship is much more complex than that. This book explores what that means for both the metaphysics and epistemology of causes - what they are and how we can find them. Across psychology, biology, and the social sciences, common themes emerge, suggesting that time plays a critical role in our understanding. The increasing availability of large time series datasets allows us to ask new questions about causality, necessitating new methods for modeling dynamic systems and incorporating mechanistic information into causal models.

This book is intended for researchers in any field (philosophy, computer science, psychology, and beyond) interested in the relationship between causality and time.

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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.

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Causality, Probability, and Time

causality, probability, and time cover

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.

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