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: 6070

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.

Matched Sampling for Causal Effects

Author: Donald B. Rubin

Publisher: Cambridge University Press

ISBN: 1139458507

Category: Mathematics

Page: N.A

View: 8861

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.

Quantitative Social Science

An Introduction

Author: Kosuke Imai

Publisher: Princeton University Press

ISBN: 1400885256

Category: Social Science

Page: 432

View: 8476

Quantitative analysis is an increasingly essential skill for social science research, yet students in the social sciences and related areas typically receive little training in it—or if they do, they usually end up in statistics classes that offer few insights into their field. This textbook is a practical introduction to data analysis and statistics written especially for undergraduates and beginning graduate students in the social sciences and allied fields, such as economics, sociology, public policy, and data science. Quantitative Social Science engages directly with empirical analysis, showing students how to analyze data using the R programming language and to interpret the results—it encourages hands-on learning, not paper-and-pencil statistics. More than forty data sets taken directly from leading quantitative social science research illustrate how data analysis can be used to answer important questions about society and human behavior. Proven in the classroom, this one-of-a-kind textbook features numerous additional data analysis exercises and interactive R programming exercises, and also comes with supplementary teaching materials for instructors. Written especially for students in the social sciences and allied fields, including economics, sociology, public policy, and data science Provides hands-on instruction using R programming, not paper-and-pencil statistics Includes more than forty data sets from actual research for students to test their skills on Covers data analysis concepts such as causality, measurement, and prediction, as well as probability and statistical tools Features a wealth of supplementary exercises, including additional data analysis exercises and interactive programming exercises Offers a solid foundation for further study Comes with additional course materials online, including notes, sample code, exercises and problem sets with solutions, and lecture slides

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: 8065

"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"--

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: 4998

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.

Counterfactuals and Causal Inference

Author: Stephen L. Morgan,Christopher Winship

Publisher: Cambridge University Press

ISBN: 1107065070

Category: Mathematics

Page: 524

View: 7109

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

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: 1717

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

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: 8789

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.

Handbook of Causal Analysis for Social Research

Author: Stephen L. Morgan

Publisher: Springer Science & Business Media

ISBN: 9400760949

Category: Social Science

Page: 424

View: 6291

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.

Propensity Score Analysis

Author: Shenyang Guo,Mark W. Fraser

Publisher: SAGE

ISBN: 1452235007

Category: Mathematics

Page: 421

View: 1018

Fully updated to reflect the most recent changes in the field, the Second Edition of Propensity Score Analysis provides an accessible, systematic review of the origins, history, and statistical foundations of propensity score analysis, illustrating how it can be used for solving evaluation and causal-inference problems. With a strong focus on practical applications, the authors explore various strategies for employing PSA, discuss the use of PSA with alternative types of data, and delineate the limitations of PSA under a variety of constraints. Unlike existing textbooks on program evaluation and causal inference, this book delves into statistical concepts, formulas, and models within the context of a robust and engaging focus on application.

Targeted Learning

Causal Inference for Observational and Experimental Data

Author: Mark J. van der Laan,Sherri Rose

Publisher: Springer Science & Business Media

ISBN: 9781441997821

Category: Mathematics

Page: 628

View: 3919

The statistics profession is at a unique point in history. The need for valid statistical tools is greater than ever; data sets are massive, often measuring hundreds of thousands of measurements for a single subject. The field is ready to move towards clear objective benchmarks under which tools can be evaluated. Targeted learning allows (1) the full generalization and utilization of cross-validation as an estimator selection tool so that the subjective choices made by humans are now made by the machine, and (2) targeting the fitting of the probability distribution of the data toward the target parameter representing the scientific question of interest. This book is aimed at both statisticians and applied researchers interested in causal inference and general effect estimation for observational and experimental data. Part I is an accessible introduction to super learning and the targeted maximum likelihood estimator, including related concepts necessary to understand and apply these methods. Parts II-IX handle complex data structures and topics applied researchers will immediately recognize from their own research, including time-to-event outcomes, direct and indirect effects, positivity violations, case-control studies, censored data, longitudinal data, and genomic studies.

Statistical Methods for Dynamic Treatment Regimes

Reinforcement Learning, Causal Inference, and Personalized Medicine

Author: Bibhas Chakraborty,Erica E.M. Moodie

Publisher: Springer Science & Business Media

ISBN: 1461474280

Category: Medical

Page: 204

View: 6643

Statistical Methods for Dynamic Treatment Regimes shares state of the art of statistical methods developed to address questions of estimation and inference for dynamic treatment regimes, a branch of personalized medicine. This volume demonstrates these methods with their conceptual underpinnings and illustration through analysis of real and simulated data. These methods are immediately applicable to the practice of personalized medicine, which is a medical paradigm that emphasizes the systematic use of individual patient information to optimize patient health care. This is the first single source to provide an overview of methodology and results gathered from journals, proceedings, and technical reports with the goal of orienting researchers to the field. The first chapter establishes context for the statistical reader in the landscape of personalized medicine. Readers need only have familiarity with elementary calculus, linear algebra, and basic large-sample theory to use this text. Throughout the text, authors direct readers to available code or packages in different statistical languages to facilitate implementation. In cases where code does not already exist, the authors provide analytic approaches in sufficient detail that any researcher with knowledge of statistical programming could implement the methods from scratch. This will be an important volume for a wide range of researchers, including statisticians, epidemiologists, medical researchers, and machine learning researchers interested in medical applications. Advanced graduate students in statistics and biostatistics will also find material in Statistical Methods for Dynamic Treatment Regimes to be a critical part of their studies.

Causality

Author: Judea Pearl

Publisher: Cambridge University Press

ISBN: 1139643983

Category: Science

Page: N.A

View: 5033

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.

Observation and Experiment

An Introduction to Causal Inference

Author: Paul R. Rosenbaum

Publisher: Harvard University Press

ISBN: 067497557X

Category: Mathematics

Page: 400

View: 7692

In the face of conflicting claims about some treatments, behaviors, and policies, the question arises: What is the most scientifically rigorous way to draw conclusions about cause and effect in the study of humans? In this introduction to causal inference, Paul Rosenbaum explains key concepts and methods through real-world examples.

Causality

Statistical Perspectives and Applications

Author: Carlo Berzuini,Philip Dawid,Luisa Bernardinell

Publisher: John Wiley & Sons

ISBN: 1119941733

Category: Mathematics

Page: 416

View: 465

A state of the art volume on statistical causality Causality: Statistical Perspectives and Applications presents a wide-ranging collection of seminal contributions by renowned experts in the field, providing a thorough treatment of all aspects of statistical causality. It covers the various formalisms in current use, methods for applying them to specific problems, and the special requirements of a range of examples from medicine, biology and economics to political science. This book: Provides a clear account and comparison of formal languages, concepts and models for statistical causality. Addresses examples from medicine, biology, economics and political science to aid the reader's understanding. Is authored by leading experts in their field. Is written in an accessible style. Postgraduates, professional statisticians and researchers in academia and industry will benefit from this book.

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: 5924

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

Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives

Author: Donald B. Rubin,Andrew Gelman,Xiao-Li Meng

Publisher: John Wiley & Sons

ISBN: 9780470090435

Category: Mathematics

Page: 407

View: 4846

This book brings together a collection of articles onstatistical methods relating to missing data analysis, includingmultiple imputation, propensity scores, instrumental variables, andBayesian inference. Covering new research topicsand real-world examples which do not feature in manystandard texts. The book is dedicated to Professor Don Rubin(Harvard). Don Rubin has made fundamental contributions tothe study of missing data. Key features of the book include: Comprehensive coverage of an imporant area for both researchand applications. Adopts a pragmatic approach to describing a wide range ofintermediate and advanced statistical techniques. Covers key topics such as multiple imputation, propensityscores, instrumental variables and Bayesian inference. Includes a number of applications from the social and healthsciences. Edited and authored by highly respected researchers in thearea.

Identification Problems in the Social Sciences

Author: Charles F. Manski

Publisher: Harvard University Press

ISBN: 9780674442849

Category: Social Science

Page: 172

View: 1850

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.

Statistical Analysis with Missing Data

Author: Roderick J. A. Little,Donald B. Rubin

Publisher: John Wiley & Sons

ISBN: 1118625889

Category: Mathematics

Page: 408

View: 960

Praise for the First Edition of Statistical Analysis with Missing Data "An important contribution to the applied statistics literature.... I give the book high marks for unifying and making accessible much of the past and current work in this important area." —William E. Strawderman, Rutgers University "This book...provide[s] interesting real-life examples, stimulating end-of-chapter exercises, and up-to-date references. It should be on every applied statistician’s bookshelf." —The Statistician "The book should be studied in the statistical methods department in every statistical agency." —Journal of Official Statistics Statistical analysis of data sets with missing values is a pervasive problem for which standard methods are of limited value. The first edition of Statistical Analysis with Missing Data has been a standard reference on missing-data methods. Now, reflecting extensive developments in Bayesian methods for simulating posterior distributions, this Second Edition by two acknowledged experts on the subject offers a thoroughly up-to-date, reorganized survey of current methodology for handling missing-data problems. Blending theory and application, authors Roderick Little and Donald Rubin review historical approaches to the subject and describe rigorous yet simple methods for multivariate analysis with missing values. They then provide a coherent theory for analysis of problems based on likelihoods derived from statistical models for the data and the missing-data mechanism and apply the theory to a wide range of important missing-data problems. The new edition now enlarges its coverage to include: Expanded coverage of Bayesian methodology, both theoretical and computational, and of multiple imputation Analysis of data with missing values where inferences are based on likelihoods derived from formal statistical models for the data-generating and missing-data mechanisms Applications of the approach in a variety of contexts including regression, factor analysis, contingency table analysis, time series, and sample survey inference Extensive references, examples, and exercises Amstat News asked three review editors to rate their top five favorite books in the September 2003 issue. Statistical Analysis With Missing Data was among those chosen.

An Introduction to Causal Inference

Author: Judea Pearl

Publisher: CreateSpace

ISBN: 9781507894293

Category:

Page: 94

View: 2725

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.