A Teia da Causalidade by Williamson Johnny Hatzinakis Brigido. Title A Teia da Causalidade. Format Paperback. Publisher Kindle Direct Publishing. GE_Item_ID:175417176;.
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The Book of Why: The New Science of Cause and Effect Pearl, Judea and Mackenzie, Dana
New Paperback
Each of these parts starts with an introduction written by Judea Pearl.
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.
Causal Inference in StatisticsA Primer Author(s): Judea Pearl, Madelyn Glymour, Nicholas P. Jewell Format: Paperback Publisher: John Wiley & Sons Inc, United States Imprint: John Wiley & Sons Inc ISBN-13: 9781119186847, 978-1119186847 Synopsis CAUSAL INFERENCE IN STATISTICS A Primer 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 statistical methods of data analysis, until now, no beginner-level book has been written about the exploding arsenal of methods that can tease causal information from data. Causal Inference in Statistics fills that gap. Using simple examples and plain language, the book lays out how to define causal parameters; the assumptions necessary to estimate causal parameters in a variety of situations; how to express those assumptions mathematically; whether those assumptions have testable implications; how to predict the effects of interventions; and how to reason counterfactually. These are the foundational tools that any student of statistics needs to acquire in order to use statistical methods to answer causal questions of interest. This book is accessible to anyone with an interest in interpreting data, from undergraduates, professors, researchers, or to the interested layperson. Examples are drawn from a wide variety of fields, including medicine, public policy, and law; a brief introduction to probability and statistics is provided for the uninitiated; and each chapter comes with study questions to reinforce the readers understanding.
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. .
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