Statistical Models For Causal Analysis
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Statistical Models For Causal Analysis
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Author : Robert D. Retherford
language : en
Publisher: Wiley-Interscience
Release Date : 1993-12-06
Statistical Models For Causal Analysis written by Robert D. Retherford and has been published by Wiley-Interscience this book supported file pdf, txt, epub, kindle and other format this book has been release on 1993-12-06 with Mathematics categories.
Supported by numerous tables and graphs, using real survey data, as well as an appendix of computer programs for the statistical packages SAS, BMDP, and LIMDEP, the book is an ideal primer for understanding and using statistical models in analytical work.
Statistical Models And Causal Inference
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Author : David A. Freedman
language : en
Publisher: Cambridge University Press
Release Date : 2010
Statistical Models And Causal Inference written by David A. Freedman and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with Mathematics categories.
David A. Freedman presents a definitive synthesis of his approach to statistical modeling and causal inference in the social sciences.
Statistical Models For Causal Analysis
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Author : Robert D. Retherford
language : en
Publisher: John Wiley & Sons
Release Date : 2011-02-01
Statistical Models For Causal Analysis written by Robert D. Retherford and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-02-01 with Mathematics categories.
Simplifies the treatment of statistical inference focusing on how to specify and interpret models in the context of testing causal theories. Simple bivariate regression, multiple regression, multiple classification analysis, path analysis, logit regression, multinomial logit regression and survival models are among the subjects covered. Features an appendix of computer programs (for major statistical packages) that are used to generate illustrative examples contained in the chapters.
Statistical Approaches To Causal Analysis
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Author : Matthew McBee
language : en
Publisher: SAGE
Release Date : 2022-03
Statistical Approaches To Causal Analysis written by Matthew McBee and has been published by SAGE this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-03 with Psychology categories.
A practical, up-to-date, step-by-step guidance on causal analysis; which features worked example datasets throughout to see methods in action. McBee clearly demonstrates techniques such as Rubin causal model, direct acyclic graphs and propensity score analysis.
The Sage Handbook Of Regression Analysis And Causal Inference
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Author : Henning Best
language : en
Publisher: SAGE
Release Date : 2013-12-20
The Sage Handbook Of Regression Analysis And Causal Inference written by Henning Best and has been published by SAGE this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-12-20 with Social Science categories.
′The editors of the new SAGE Handbook of Regression Analysis and Causal Inference have assembled a wide-ranging, high-quality, and timely collection of articles on topics of central importance to quantitative social research, many written by leaders in the field. Everyone engaged in statistical analysis of social-science data will find something of interest in this book.′ - John Fox, Professor, Department of Sociology, McMaster University ′The authors do a great job in explaining the various statistical methods in a clear and simple way - focussing on fundamental understanding, interpretation of results, and practical application - yet being precise in their exposition.′ - Ben Jann, Executive Director, Institute of Sociology, University of Bern ′Best and Wolf have put together a powerful collection, especially valuable in its separate discussions of uses for both cross-sectional and panel data analysis.′ -Tom Smith, Senior Fellow, NORC, University of Chicago Edited and written by a team of leading international social scientists, this Handbook provides a comprehensive introduction to multivariate methods. The Handbook focuses on regression analysis of cross-sectional and longitudinal data with an emphasis on causal analysis, thereby covering a large number of different techniques including selection models, complex samples, and regression discontinuities. Each Part starts with a non-mathematical introduction to the method covered in that section, giving readers a basic knowledge of the method’s logic, scope and unique features. Next, the mathematical and statistical basis of each method is presented along with advanced aspects. Using real-world data from the European Social Survey (ESS) and the Socio-Economic Panel (GSOEP), the book provides a comprehensive discussion of each method’s application, making this an ideal text for PhD students and researchers embarking on their own data analysis.
Causal Analysis With Panel Data
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Author : Steven E. Finkel
language : en
Publisher: SAGE
Release Date : 1995-01-17
Causal Analysis With Panel Data written by Steven E. Finkel and has been published by SAGE this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995-01-17 with Medical categories.
Panel data, which consist of information gathered from the same individuals or units at several different points in time, are commonly used in the social sciences to test theories of individual and social change. This book provides an overview of models that are appropriate for the analysis of panel data, focusing specifically on the area where panels offer major advantages over cross-sectional research designs: the analysis of causal interrelationships among variables. Without "painting" panel data as a cure all for the problems of causal inference in nonexperimental research, the author shows how panel data offer multiple ways of strengthening the causal inference process. In addition, he shows how to estimate models that contain a variety of lag specifications, reciprocal effects, and imperfectly measured variables. Appropriate for readers who are familiar with multiple regression analysis and causal modeling, this book will offer readers the highlights of developments in this technique from diverse disciplines to analytic traditions.
Causal Inference In Statistics
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Author : Judea Pearl
language : en
Publisher: John Wiley & Sons
Release Date : 2016-01-25
Causal Inference In Statistics written by Judea Pearl and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-01-25 with Mathematics categories.
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.
Applied Bayesian Modeling And Causal Inference From Incomplete Data Perspectives
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Author : Andrew Gelman
language : en
Publisher: John Wiley & Sons
Release Date : 2004-09-03
Applied Bayesian Modeling And Causal Inference From Incomplete Data Perspectives written by Andrew Gelman and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-09-03 with Mathematics categories.
This book brings together a collection of articles on statistical methods relating to missing data analysis, including multiple imputation, propensity scores, instrumental variables, and Bayesian inference. Covering new research topics and real-world examples which do not feature in many standard texts. The book is dedicated to Professor Don Rubin (Harvard). Don Rubin has made fundamental contributions to the study of missing data. Key features of the book include: Comprehensive coverage of an imporant area for both research and applications. Adopts a pragmatic approach to describing a wide range of intermediate and advanced statistical techniques. Covers key topics such as multiple imputation, propensity scores, instrumental variables and Bayesian inference. Includes a number of applications from the social and health sciences. Edited and authored by highly respected researchers in the area.
The Book Of Why
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Author : Judea Pearl
language : en
Publisher: Penguin UK
Release Date : 2018-05-15
The Book Of Why written by Judea Pearl and has been published by Penguin UK this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-05-15 with Philosophy categories.
The hugely influential book on how the understanding of causality revolutionized science and the world, by the pioneer of artificial intelligence 'Wonderful ... illuminating and fun to read' Daniel Kahneman, Nobel Prize-winner and author of Thinking, Fast and Slow 'Correlation does not imply causation.' For decades, this mantra was invoked by scientists in order to avoid taking positions as to whether one thing caused another, such as smoking and cancer, or carbon dioxide and global warming. But today, that taboo is dead. The causal revolution, sparked by world-renowned computer scientist Judea Pearl and his colleagues, has cut through a century of confusion and placed cause and effect on a firm scientific basis. Now, Pearl and science journalist Dana Mackenzie explain causal thinking to general readers for the first time, showing how it allows us to explore the world that is and the worlds that could have been. It is the essence of human and artificial intelligence. And just as Pearl's discoveries have enabled machines to think better, The Book of Why explains how we too can think better. 'Pearl's accomplishments over the last 30 years have provided the theoretical basis for progress in artificial intelligence and have redefined the term "thinking machine"' Vint Cerf
Data Analysis Using Regression And Multilevel Hierarchical Models
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Author : Andrew Gelman
language : en
Publisher: Cambridge University Press
Release Date : 2007
Data Analysis Using Regression And Multilevel Hierarchical Models written by Andrew Gelman and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Mathematics categories.
This book, first published in 2007, is for the applied researcher performing data analysis using linear and nonlinear regression and multilevel models.