Covering a topic applicable to fields ranging from education to health care to psychology, this book provides a broad critical analysis of the assumptions that researchers and practitioners have about causation and explains how readers can improve their thinking about causation.
"Covering a topic applicable to fields ranging from education to health care to psychology, this book provides a broad critical analysis of the assumptions that researchers and practitioners have about causation and explains how readers can improve their thinking about causation"
"Covering a topic applicable to fields ranging from education to health care to psychology, this book provides a broad critical analysis of the assumptions that researchers and practitioners have about causation and explains how readers can improve their thinking about causation…
"Covering a topic applicable to fields ranging from education to health care to psychology, this book provides a broad critical analysis of the assumptions that researchers and practitioners have about causation and explains how readers can improve their thinking about causation…
Causal Inference in Statistics: A Primer Judea Pearl, Computer Science and Statistics, University of California Los Angeles, USA Madelyn Glymour, Philosophy, Carnegie Mellon University, Pittsburgh, USA and Nicholas P. Jewell, Biostatistics, University of California, Berkeley, US…
Causal Inference in Statistics: A Primer Judea Pearl, Computer Science and Statistics, University of California Los Angeles, USA Madelyn Glymour, Philosophy, Carnegie Mellon University, Pittsburgh, USA and Nicholas P. Jewell, Biostatistics, University of California, Berkeley, US…
Many of the concepts and terminology surrounding modern causal inference can be quite intimidating to the novice. Judea Pearl presents a book ideal for beginners in statistics, providing a comprehensive introduction to the field of causality. Examples from classical statistics a…
Causal Inference in Statistics: A Primer Judea Pearl, Computer Science and Statistics, University of California Los Angeles, USA Madelyn Glymour, Philosophy, Carnegie Mellon University, Pittsburgh, USA and Nicholas P. Jewell, Biostatistics, University of California, Berkeley, US…
"Causality is central to the understanding and use of data. Without an understanding of cause effect relationships, we cannot use data to answer questions as basic as, "Does this treatment harm or help patients'" But though hundreds of introductory texts are available on statist…
