This book provides a comprehensive overview of the theory of causal inference, emphasizing the distinction between correlation and causation. It explores the various methods for identifying causal relationships and examines their implications across different fields, including statistics, computer science, and philosophy. .
Cited in more than 2,100 scientific publications, it continues to liberate scientists from the traditional molds of statistical thinking. Causality will be of interest to students and professionals in a wide variety of fields.
Written by one of the preeminent researchers in the field, this book provides a comprehensive exposition of modern analysis of causation. Cited in more than 2,100 scientific publications, it continues to liberate scientists from the traditional molds of statistical thinking.
Cited in more than 2,100 scientific publications, it continues to liberate scientists from the traditional molds of statistical thinking. Causality will be of interest to students and professionals in a wide variety of fields.
Written by one of the preeminent researchers in the field, this book provides a comprehensive exposition of modern analysis of causation. Students in these fields will find natural models, simple inferential procedures, and precise mathematical definitions of causal concepts that traditional texts have evaded or made unduly complicated.
Written by one of the preeminent researchers in the field, this book provides a comprehensive exposition of modern analysis of causation. Students in these fields will find natural models, simple inferential procedures, and precise mathematical definitions of causal concepts that traditional texts have evaded or made unduly complicated.
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