'Correlation does not imply causation.' For decades, this mantra was invoked by scientists in order to avoid taking positions as to whether one thing caused another, such as smoking and cancer, or carbon dioxide and global warming. But today, that...
Causality is central to the understanding and use of data. Without an understanding of causeâeffect relationships, we cannot use data to answer questions as basic as "Does this treatment harm or help patients?" But though hundreds...
This book summarizes recent advances in causal inference and underscores the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions...
Written by one of the preeminent researchers in the field, this book provides a comprehensive exposition of modern analysis of causation. It shows how causality has grown from a nebulous concept into a mathematical theory with significant applications...