Statistical Approaches To Causal Analysis

Author : Matthew McBee
Publisher : SAGE
Page : 178 pages
File Size : 41,5 Mb
Release : 2022-03-01
Category : Social Science
ISBN : 9781529711110

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Statistical Approaches to Causal Analysis by Matthew McBee Pdf

This book provides an up-to-date and accessible introduction to causal inference in quantitative research. Featuring worked example datasets throughout, it clearly outlines the steps involved in carrying out various types of statistical causal analysis. In turn, helping you apply these methods to your own research. It contains guidance on: Selecting the most appropriate conditioning method for your data. Applying the Rubin’s Causal Model to your analysis, a mathematical framework for understanding and ensuring accurate causation inferences. Utilising various techniques and designs, such as propensity scores, instrumental variables analysis, and regression discontinuity designs, to better synthesise and analyse different types of data. Part of The SAGE Quantitative Research Kit, this book will give you the know-how and confidence needed to succeed on your quantitative research journey.

Author : Judea Pearl,Madelyn Glymour,Nicholas P. Jewell
Publisher : John Wiley & Sons
Page : 160 pages
File Size : 55,5 Mb
Release : 2016-02-03
Category : Mathematics
ISBN : 9781119186854

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Causal Inference in Statistics by Judea Pearl,Madelyn Glymour,Nicholas P. Jewell Pdf

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.

Author : Wolfgang Wiedermann,Alexander von Eye
Publisher : John Wiley & Sons
Page : 478 pages
File Size : 50,9 Mb
Release : 2016-06-07
Category : Social Science
ISBN : 9781118947043

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Statistics and Causality by Wolfgang Wiedermann,Alexander von Eye Pdf

b”STATISTICS AND CAUSALITYA one-of-a-kind guide to identifying and dealing with modern statistical developments in causality Written by a group of well-known experts, Statistics and Causality: Methods for Applied Empirical Research focuses on the most up-to-date developments in statistical methods in respect to causality. Illustrating the properties of statistical methods to theories of causality, the book features a summary of the latest developments in methods for statistical analysis of causality hypotheses. The book is divided into five accessible and independent parts. The first part introduces the foundations of causal structures and discusses issues associated with standard mechanistic and difference-making theories of causality. The second part features novel generalizations of methods designed to make statements concerning the direction of effects. The third part illustrates advances in Granger-causality testing and related issues. The fourth part focuses on counterfactual approaches and propensity score analysis. Finally, the fifth part presents designs for causal inference with an overview of the research designs commonly used in epidemiology. Statistics and Causality: Methods for Applied Empirical Research also includes: New statistical methodologies and approaches to causal analysis in the context of the continuing development of philosophical theories End-of-chapter bibliographies that provide references for further discussions and additional research topics Discussions on the use and applicability of software when appropriate Statistics and Causality: Methods for Applied Empirical Research is an ideal reference for practicing statisticians, applied mathematicians, psychologists, sociologists, logicians, medical professionals, epidemiologists, and educators who want to learn more about new methodologies in causal analysis. The book is also an excellent textbook for graduate-level courses in causality and qualitative logic.

Author : Henning Best,Christof Wolf
Publisher : SAGE
Page : 424 pages
File Size : 47,7 Mb
Release : 2014-09-27
Category : Reference
ISBN : 9781473908352

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The SAGE Handbook of Regression Analysis and Causal Inference by Henning Best,Christof Wolf Pdf

'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.

Author : Shenyang Guo,Mark W. Fraser
Publisher : SAGE
Page : 449 pages
File Size : 46,9 Mb
Release : 2015
Category : Mathematics
ISBN : 9781452235004

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Propensity Score Analysis by Shenyang Guo,Mark W. Fraser Pdf

Provides readers with a systematic review of the origins, history, and statistical foundations of Propensity Score Analysis (PSA) and illustrates how it can be used for solving evaluation and causal-inference problems.

Author : Henriette Engelhardt,Hans-Peter Kohler,Alexia Fürnkranz-Prskawetz
Publisher : Springer Science & Business Media
Page : 253 pages
File Size : 44,8 Mb
Release : 2009-05-05
Category : Social Science
ISBN : 9781402099670

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Causal Analysis in Population Studies by Henriette Engelhardt,Hans-Peter Kohler,Alexia Fürnkranz-Prskawetz Pdf

The central aim of many studies in population research and demography is to explain cause-effect relationships among variables or events. For decades, population scientists have concentrated their efforts on estimating the ‘causes of effects’ by applying standard cross-sectional and dynamic regression techniques, with regression coefficients routinely being understood as estimates of causal effects. The standard approach to infer the ‘effects of causes’ in natural sciences and in psychology is to conduct randomized experiments. In population studies, experimental designs are generally infeasible. In population studies, most research is based on non-experimental designs (observational or survey designs) and rarely on quasi experiments or natural experiments. Using non-experimental designs to infer causal relationships—i.e. relationships that can ultimately inform policies or interventions—is a complex undertaking. Specifically, treatment effects can be inferred from non-experimental data with a counterfactual approach. In this counterfactual perspective, causal effects are defined as the difference between the potential outcome irrespective of whether or not an individual had received a certain treatment (or experienced a certain cause). The counterfactual approach to estimate effects of causes from quasi-experimental data or from observational studies was first proposed by Rubin in 1974 and further developed by James Heckman and others. This book presents both theoretical contributions and empirical applications of the counterfactual approach to causal inference.

Author : Silvia Bacci,Bruno Chiandotto
Publisher : CRC Press
Page : 305 pages
File Size : 47,6 Mb
Release : 2019-07-11
Category : Mathematics
ISBN : 9781351621397

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Introduction to Statistical Decision Theory by Silvia Bacci,Bruno Chiandotto Pdf

Introduction to Statistical Decision Theory: Utility Theory and Causal Analysis provides the theoretical background to approach decision theory from a statistical perspective. It covers both traditional approaches, in terms of value theory and expected utility theory, and recent developments, in terms of causal inference. The book is specifically designed to appeal to students and researchers that intend to acquire a knowledge of statistical science based on decision theory. Features Covers approaches for making decisions under certainty, risk, and uncertainty Illustrates expected utility theory and its extensions Describes approaches to elicit the utility function Reviews classical and Bayesian approaches to statistical inference based on decision theory Discusses the role of causal analysis in statistical decision theory

Author : Stephen L. Morgan
Publisher : Springer Science & Business Media
Page : 423 pages
File Size : 47,8 Mb
Release : 2013-04-22
Category : Social Science
ISBN : 9789400760943

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Handbook of Causal Analysis for Social Research by Stephen L. Morgan Pdf

What constitutes a causal explanation, and must an explanation be causal? What warrants a causal inference, as opposed to a descriptive regularity? What techniques are available to detect when causal effects are present, and when can these techniques be used to identify the relative importance of these effects? What complications do the interactions of individuals create for these techniques? When can mixed methods of analysis be used to deepen causal accounts? Must causal claims include generative mechanisms, and how effective are empirical methods designed to discover them? The Handbook of Causal Analysis for Social Research tackles these questions with nineteen chapters from leading scholars in sociology, statistics, public health, computer science, and human development.

Author : Robert D. Retherford,Minja Kim Choe
Publisher : John Wiley & Sons
Page : 274 pages
File Size : 47,6 Mb
Release : 2011-02-01
Category : Mathematics
ISBN : 9781118031346

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Statistical Models for Causal Analysis by Robert D. Retherford,Minja Kim Choe Pdf

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.

Author : David A. Freedman
Publisher : Cambridge University Press
Page : 416 pages
File Size : 47,7 Mb
Release : 2010
Category : Mathematics
ISBN : 9780521195003

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Statistical Models and Causal Inference by David A. Freedman Pdf

David A. Freedman presents a definitive synthesis of his approach to statistical modeling and causal inference in the social sciences.

Author : Guanglei Hong
Publisher : John Wiley & Sons
Page : 448 pages
File Size : 41,5 Mb
Release : 2015-06-09
Category : Mathematics
ISBN : 9781119030607

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Causality in a Social World by Guanglei Hong Pdf

Causality in a Social World introduces innovative new statistical research and strategies for investigating moderated intervention effects, mediated intervention effects, and spill-over effects using experimental or quasi-experimental data. The book uses potential outcomes to define causal effects, explains and evaluates identification assumptions using application examples, and compares innovative statistical strategies with conventional analysis methods. Whilst highlighting the crucial role of good research design and the evaluation of assumptions required for identifying causal effects in the context of each application, the author demonstrates that improved statistical procedures will greatly enhance the empirical study of causal relationship theory. Applications focus on interventions designed to improve outcomes for participants who are embedded in social settings, including families, classrooms, schools, neighbourhoods, and workplaces.

Author : Hans-Peter Blossfeld,Götz Rohwer
Publisher : Psychology Press
Page : 310 pages
File Size : 43,9 Mb
Release : 2002
Category : Psychology
ISBN : 9780805840902

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Techniques of Event History Modeling by Hans-Peter Blossfeld,Götz Rohwer Pdf

Including new developments and publications which have appeared since the publication of the first edition in 1995, this second edition: *gives a comprehensive introductory account of event history modeling techniques and their use in applied research in economics and the social sciences; *demonstrates that event history modeling is a major step forward in causal analysis. To do so the authors show that event history models employ the time-path of changes in states and relate changes in causal variables in the past to changes in discrete outcomes in the future; and *introduces the reader to the computer program Transition Data Analysis (TDA). This software estimates the sort of models most frequently used with longitudinal data, in particular, discrete-time and continuous-time event history data. Techniques of Event History Modeling can serve as a student textbook in the fields of statistics, economics, the social sciences, psychology, and the political sciences. It can also be used as a reference for scientists in all fields of research.

Author : Andrew Gelman,Xiao-Li Meng
Publisher : John Wiley & Sons
Page : 448 pages
File Size : 50,5 Mb
Release : 2004-09-03
Category : Mathematics
ISBN : 047009043X

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Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives by Andrew Gelman,Xiao-Li Meng Pdf

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.

Author : Guido W. Imbens,Donald B. Rubin
Publisher : Cambridge University Press
Page : 647 pages
File Size : 44,8 Mb
Release : 2015-04-06
Category : Business & Economics
ISBN : 9780521885881

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Causal Inference in Statistics, Social, and Biomedical Sciences by Guido W. Imbens,Donald B. Rubin Pdf

This text presents statistical methods for studying causal effects and discusses how readers can assess such effects in simple randomized experiments.

Author : Vaughn R. McKim,Stephen P. Turner
Publisher : Unknown
Page : 432 pages
File Size : 49,9 Mb
Release : 1997
Category : Mathematics
ISBN : STANFORD:36105019375315

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Causality in Crisis? by Vaughn R. McKim,Stephen P. Turner Pdf