Counterfactuals and Causal Inference

Author: Stephen L. Morgan,Christopher Winship

Publisher: Cambridge University Press

ISBN: 1107065070

Category: Mathematics

Page: 524

View: 8616


This new edition aims to convince social scientists to take a counterfactual approach to the core questions of their fields.

Handbook of Causal Analysis for Social Research

Author: Stephen L. Morgan

Publisher: Springer Science & Business Media

ISBN: 9400760949

Category: Social Science

Page: 424

View: 6839


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.

Matched Sampling for Causal Effects

Author: Donald B. Rubin

Publisher: Cambridge University Press

ISBN: 1139458507

Category: Mathematics

Page: N.A

View: 352


Matched sampling is often used to help assess the causal effect of some exposure or intervention, typically when randomized experiments are not available or cannot be conducted. This book presents a selection of Donald B. Rubin's research articles on matched sampling, from the early 1970s, when the author was one of the major researchers involved in establishing the field, to recent contributions to this now extremely active area. The articles include fundamental theoretical studies that have become classics, important extensions, and real applications that range from breast cancer treatments to tobacco litigation to studies of criminal tendencies. They are organized into seven parts, each with an introduction by the author that provides historical and personal context and discusses the relevance of the work today. A concluding essay offers advice to investigators designing observational studies. The book provides an accessible introduction to the study of matched sampling and will be an indispensable reference for students and researchers.

Causal Inference in Statistics

A Primer

Author: Judea Pearl,Madelyn Glymour,Nicholas P. Jewell

Publisher: John Wiley & Sons

ISBN: 1119186862

Category: Mathematics

Page: 160

View: 6298


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.

Causal Inference for Statistics, Social, and Biomedical Sciences

An Introduction

Author: Guido W. Imbens,Donald B. Rubin

Publisher: Cambridge University Press

ISBN: 1316094391

Category: Mathematics

Page: N.A

View: 3997


Most questions in social and biomedical sciences are causal in nature: what would happen to individuals, or to groups, if part of their environment were changed? In this groundbreaking text, two world-renowned experts present statistical methods for studying such questions. This book starts with the notion of potential outcomes, each corresponding to the outcome that would be realized if a subject were exposed to a particular treatment or regime. In this approach, causal effects are comparisons of such potential outcomes. The fundamental problem of causal inference is that we can only observe one of the potential outcomes for a particular subject. The authors discuss how randomized experiments allow us to assess causal effects and then turn to observational studies. They lay out the assumptions needed for causal inference and describe the leading analysis methods, including matching, propensity-score methods, and instrumental variables. Many detailed applications are included, with special focus on practical aspects for the empirical researcher.

Statistical Models and Causal Inference

A Dialogue with the Social Sciences

Author: David A. Freedman,David Collier,Jasjeet S. Sekhon

Publisher: Cambridge University Press

ISBN: 0521195004

Category: Mathematics

Page: 399

View: 3178


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


Author: Judea Pearl

Publisher: Cambridge University Press

ISBN: 1139643983

Category: Science

Page: N.A

View: 3517


Written by one of the preeminent researchers in the field, this book provides a comprehensive exposition of modern analysis of causation. It shows how causality has grown from a nebulous concept into a mathematical theory with significant applications in the fields of statistics, artificial intelligence, economics, philosophy, cognitive science, and the health and social sciences. Judea Pearl presents and unifies the probabilistic, manipulative, counterfactual, and structural approaches to causation and devises simple mathematical tools for studying the relationships between causal connections and statistical associations. Cited in more than 2,100 scientific publications, it continues to liberate scientists from the traditional molds of statistical thinking. In this revised edition, Judea Pearl elucidates thorny issues, answers readers' questions, and offers a panoramic view of recent advances in this field of research. Causality will be of interest to students and professionals in a wide variety of fields. Dr Judea Pearl has received the 2011 Rumelhart Prize for his leading research in Artificial Intelligence (AI) and systems from The Cognitive Science Society.

Natural Experiments in the Social Sciences

A Design-Based Approach

Author: Thad Dunning

Publisher: Cambridge University Press

ISBN: 1107017661

Category: Business & Economics

Page: 358

View: 1405


The first comprehensive guide to natural experiments, providing an ideal introduction for scholars and students.

Computational Social Science

Discovery and Prediction

Author: R. Michael Alvarez

Publisher: Cambridge University Press

ISBN: 1316531287

Category: Political Science

Page: N.A

View: 8498


Quantitative research in social science research is changing rapidly. Researchers have vast and complex arrays of data with which to work: we have incredible tools to sift through the data and recognize patterns in that data; there are now many sophisticated models that we can use to make sense of those patterns; and we have extremely powerful computational systems that help us accomplish these tasks quickly. This book focuses on some of the extraordinary work being conducted in computational social science - in academia, government, and the private sector - while highlighting current trends, challenges, and new directions. Thus, Computational Social Science showcases the innovative methodological tools being developed and applied by leading researchers in this new field. The book shows how academics and the private sector are using many of these tools to solve problems in social science and public policy.

Explanation in Causal Inference

Methods for Mediation and Interaction

Author: Tyler VanderWeele,Tyler J.. VanderWeele

Publisher: Oxford University Press, USA

ISBN: 0199325871

Category: Psychology

Page: 706

View: 5124


"A comprehensive book on methods for mediation and interaction. The only book to approach this topic from the perspective of causal inference. Numerous software tools provided. Easy-to-read and accessible. Examples drawn from diverse fields. An essential reference for anyone conducting empirical research in the biomedical or social sciences"--

The SAGE Handbook of Regression Analysis and Causal Inference

Author: Henning Best,Christof Wolf

Publisher: SAGE

ISBN: 1473908353

Category: Social Science

Page: 424

View: 7273


'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.

Design of Observational Studies

Author: Paul R. Rosenbaum

Publisher: Springer Science & Business Media

ISBN: 1441912134

Category: Mathematics

Page: 384

View: 4095


An observational study is an empiric investigation of effects caused by treatments when randomized experimentation is unethical or infeasible. Observational studies are common in most fields that study the effects of treatments on people, including medicine, economics, epidemiology, education, psychology, political science and sociology. The quality and strength of evidence provided by an observational study is determined largely by its design. Design of Observational Studies is both an introduction to statistical inference in observational studies and a detailed discussion of the principles that guide the design of observational studies. Design of Observational Studies is divided into four parts. Chapters 2, 3, and 5 of Part I cover concisely, in about one hundred pages, many of the ideas discussed in Rosenbaum’s Observational Studies (also published by Springer) but in a less technical fashion. Part II discusses the practical aspects of using propensity scores and other tools to create a matched comparison that balances many covariates. Part II includes a chapter on matching in R. In Part III, the concept of design sensitivity is used to appraise the relative ability of competing designs to distinguish treatment effects from biases due to unmeasured covariates. Part IV discusses planning the analysis of an observational study, with particular reference to Sir Ronald Fisher’s striking advice for observational studies, "make your theories elaborate." The second edition of his book, Observational Studies, was published by Springer in 2002.

Spatial Analysis for the Social Sciences

Author: David Darmofal

Publisher: Cambridge University Press

ISBN: 0521888263

Category: Political Science

Page: 258

View: 3547


This book shows how to model the spatial interactions between actors that are at the heart of the social sciences.

A User's Guide to Path Analysis

Author: Moses E. Olobatuyi

Publisher: University Press of America

ISBN: 9780761832300

Category: Social Science

Page: 171

View: 4732


Written for graduate level students in advanced statistics, this handbook offers a comprehensive and practical overview of path analysis. A User's Guide to Path Analysis contains: Definition and graphical illustrations of basic terms and concepts Illustration of causal diagrams with emphasis on variable positioning, path symbols, error terms, missing arrows, and feedback loops In-depth discussion of assumptions underlying path analysis Discussion of causal model estimation with illustrations Practical research questions for interpreting a path model Instructions on how to read a path diagram, and how to use the SPSS computer program and interpret the results Suggestions for what to include when writing up or interpreting findings"

Methods Matter: Improving Causal Inference in Educational and Social Science Research

Author: Richard J. Murnane,John B. Willett

Publisher: Oxford University Press

ISBN: 0199780315

Category: Psychology

Page: 416

View: 4574


Educational policy-makers around the world constantly make decisions about how to use scarce resources to improve the education of children. Unfortunately, their decisions are rarely informed by evidence on the consequences of these initiatives in other settings. Nor are decisions typically accompanied by well-formulated plans to evaluate their causal impacts. As a result, knowledge about what works in different situations has been very slow to accumulate. Over the last several decades, advances in research methodology, administrative record keeping, and statistical software have dramatically increased the potential for researchers to conduct compelling evaluations of the causal impacts of educational interventions, and the number of well-designed studies is growing. Written in clear, concise prose, Methods Matter: Improving Causal Inference in Educational and Social Science Research offers essential guidance for those who evaluate educational policies. Using numerous examples of high-quality studies that have evaluated the causal impacts of important educational interventions, the authors go beyond the simple presentation of new analytical methods to discuss the controversies surrounding each study, and provide heuristic explanations that are also broadly accessible. Murnane and Willett offer strong methodological insights on causal inference, while also examining the consequences of a wide variety of educational policies implemented in the U.S. and abroad. Representing a unique contribution to the literature surrounding educational research, this landmark text will be invaluable for students and researchers in education and public policy, as well as those interested in social science.

Elements of Causal Inference

Foundations and Learning Algorithms

Author: Jonas Peters,Dominik Janzing,Bernhard Schölkopf

Publisher: MIT Press

ISBN: 0262037319

Category: Computers

Page: 288

View: 9177


The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-contained and concise introduction to causal models and how to learn them from data. After explaining the need for causal models and discussing some of the principles underlying causal inference, the book teaches readers how to use causal models: how to compute intervention distributions, how to infer causal models from observational and interventional data, and how causal ideas could be exploited for classical machine learning problems. All of these topics are discussed first in terms of two variables and then in the more general multivariate case. The bivariate case turns out to be a particularly hard problem for causal learning because there are no conditional independences as used by classical methods for solving multivariate cases. The authors consider analyzing statistical asymmetries between cause and effect to be highly instructive, and they report on their decade of intensive research into this problem. The book is accessible to readers with a background in machine learning or statistics, and can be used in graduate courses or as a reference for researchers. The text includes code snippets that can be copied and pasted, exercises, and an appendix with a summary of the most important technical concepts.

Statistics and Causality

Methods for Applied Empirical Research

Author: Wolfgang Wiedermann,Alexander von Eye

Publisher: John Wiley & Sons

ISBN: 1118947053

Category: Social Science

Page: 480

View: 4055


A 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.

Big Data and Social Science

A Practical Guide to Methods and Tools

Author: Ian Foster,Rayid Ghani,Ron S. Jarmin,Frauke Kreuter,Julia Lane

Publisher: CRC Press

ISBN: 1498751431

Category: Mathematics

Page: 376

View: 6235


Both Traditional Students and Working Professionals Acquire the Skills to Analyze Social Problems. Big Data and Social Science: A Practical Guide to Methods and Tools shows how to apply data science to real-world problems in both research and the practice. The book provides practical guidance on combining methods and tools from computer science, statistics, and social science. This concrete approach is illustrated throughout using an important national problem, the quantitative study of innovation. The text draws on the expertise of prominent leaders in statistics, the social sciences, data science, and computer science to teach students how to use modern social science research principles as well as the best analytical and computational tools. It uses a real-world challenge to introduce how these tools are used to identify and capture appropriate data, apply data science models and tools to that data, and recognize and respond to data errors and limitations. For more information, including sample chapters and news, please visit the author's website.

Identification Problems in the Social Sciences

Author: Charles F. Manski

Publisher: Harvard University Press

ISBN: 9780674442849

Category: Social Science

Page: 172

View: 6438


The author draws on examples from a range of disciplines to provide social and behavioural scientists with a toolkit for finding bounds when predicting behaviours based upon nonexperimental and experimental data.

An Introduction to Causal Inference

Author: Judea Pearl

Publisher: CreateSpace

ISBN: 9781507894293


Page: 94

View: 5729


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. Special emphasis is placed on the assumptions that underlie all causal inferences, the languages used in formulating those assumptions, the conditional nature of all causal and counterfactual claims, and the methods that have been developed for the assessment of such claims. These advances are illustrated using a general theory of causation based on the Structural Causal Model (SCM), which subsumes and unifies other approaches to causation, and provides a coherent mathematical foundation for the analysis of causes and counterfactuals. In particular, the paper surveys the development of mathematical tools for inferring (from a combination of data and assumptions) answers to three types of causal queries: those about (1) the effects of potential interventions, (2) probabilities of counterfactuals, and (3) direct and indirect effects (also known as "mediation"). Finally, the paper defines the formal and conceptual relationships between the structural and potential-outcome frameworks and presents tools for a symbiotic analysis that uses the strong features of both. The tools are demonstrated in the analyses of mediation, causes of effects, and probabilities of causation.