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 are presented throughout to demonstrate the need for causality in resolving decision-making dilemmas posed by data. Causal methods are also compared to traditional statistical methods, whilst questions are provided at the end of each section to aid student learning.
The book will open the way for including causal analysis in the standard curriculum of statistics, artificial intelligence .
This text presents statistical methods for studying causal effects and discusses how readers can assess such effects in simple randomized experiments.
Datasets, R code, and solutions to odd-numbered exercises are available on the book's website at www.routledge.com/9780367705053. Instructors can also find slides based on the book, and a full solutions manual under 'Instructor Resources'.
The Goldfield-Mantel Stratification procedure is used throughout. Raw Truncated Extensive cases 9/4.41 : 2.04 9/10.2 I 0.88 All cases 21/7.40 : 2.84 21/17.5 : 1.20 Sanctions 29/7.40 : 3.92 29/17.5 : 1.66 ness multiplies the relative ...
These advances are illustrated using a general theory of causation based on the Structural Causal Model (SCM) described in Pearl (2000a), which subsumes and unifies other approaches to causation, and provides a coherent mathematical ...
This book offers a self-contained and concise introduction to causal models and how to learn them from data.
Researchers and data analysts in public health and biomedical research will also find this book to be an important reference. This book compiles and presents new developments in statistical causal inference.
Causal Inference: The Mixtape uses legit real-world examples that I found genuinely thought-provoking. It's rare that a book prompts readers to expand their outlook; this one did for me.
It shows us the essence of human thought and key to artificial intelligence. Anyone who wants to understand either needs The Book of Why.
Is authored by leading experts in their field. Is written in an accessible style. Postgraduates, professional statisticians and researchers in academia and industry will benefit from this book