Causal Inference in Statistics - A Primer | A Primer
Lingua: inglese
Editore: John Wiley & Sons Inc, 2016
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Causal Inference in Statistics - A Primer | A Primer | Judea Pearl (u. a.) | Taschenbuch | About the Authors ixPreface xiList of Figures xvAbout the Companion Website xix1 Preliminaries: Statistical and Causal Models 11.1 Why Study Causation 11.2 Simpson's Paradox 11.3 Probability and Statistics 71.3.1 Variables 71.3.2 Events 81.3.3 Conditiona | Englisch | 2016 | John Wiley & Sons Inc | EAN 9781119186847 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.
Codice articolo 104214931
- Titolo
- Causal Inference in Statistics - A Primer | A Primer
- Autore
- Judea Pearl (u. a.)
- Editore
- John Wiley & Sons Inc
- Anno di pubblicazione
- 2016
- Condizione
- Neu
- Rilegatura
- Taschenbuch
- Lingua
- inglese
- ISBN 10
- 1119186846
- ISBN 13
- 9781119186847
- Peso dell'articolo
- 292 grammi
- Dimensioni
- 244 x 172 x 15 mm
- Cataloghi dei venditori
- Bücher
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.
"Riassunto" può appartenere a un’altra edizione di questo titolo.
Informazioni sull’autore
Judea Pearl, Computer Science and Statistics, University of California, Los Angeles, USA.
Madelyn Glymour, Philosophy, Carnegie Mellon University, Pittsburgh, USA.
Nicholas P. Jewell, Biostatistics and Statistics, University of California, Berkeley, USA.
"Descrizione articolo" può appartenere a un’altra edizione di questo titolo.
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