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: 5793

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.
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An Investigation of the Causal Inference between Epidemiology and Jurisprudence

Author: Minsoo Jung

Publisher: Springer

ISBN: 9811078629

Category: Philosophy

Page: 108

View: 320

This book examines how legal causation inference and epidemiological causal inference can be harmonized within the realm of jurisprudence, exploring why legal causation and epidemiological causation differ from each other and defining related problems. The book also discusses how legal justice can be realized and how victims’ rights can be protected. It looks at epidemiological evidence pertaining to causal relationships in cases such as smoking and the development of lung cancer, and enables readers to correctly interpret and rationally use the results of epidemiological studies in lawsuits. The book argues that in today’s risk society, it is no longer possible to thwart the competence of evidence using epidemiological research results. In particular, it points out that the number of cases that struggle to prove a causal relationship excluding those using epidemiological data will lead to an increase in the number of lawsuits for damages that arise as a result of harmful materials that affect our health. The book argues that the responsibility to compensate for damages that have actually occurred must be imputed to a particular party and that this can be achieved by understanding causal inferences between jurisprudence and epidemiology. This book serves as a foundation for students, academics and researchers who have an interest in epidemiology and the law, and those who are keen to discover how jurisprudence can bring these two areas together.
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Contemporary Philosophy and Social Science

An Interdisciplinary Dialogue

Author: Michiru Nagatsu,Attilia Ruzzene

Publisher: Bloomsbury Publishing

ISBN: 1474248772

Category: Philosophy

Page: 408

View: 7996

How should we theorize about the social world? How can we integrate theories, models and approaches from seemingly incompatible disciplines? Does theory affect social reality? This state-of-the-art collection addresses contemporary methodological questions and interdisciplinary developments in the philosophy of social science. Facilitating a mutually enriching dialogue, chapters by leading social scientists are followed by critical evaluations from philosophers of social science. This exchange showcases recent major theoretical and methodological breakthroughs and challenges in the social sciences, as well as fruitful ways in which the analytic tools developed in philosophy of science can be applied to understand these advancements. The volume covers a diverse range of principles, methods, innovations and applications, including scientific and methodological pluralism, performativity of theories, causal inferences and applications of social science to policy and business. Taking a practice-orientated and interactive approach, it offers a new philosophy of social science grounded in and relevant to the emerging social science practice.
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Exploratory Causal Analysis with Time Series Data

Author: James M. McCracken

Publisher: Morgan & Claypool Publishers

ISBN: 1627059342

Category: Computers

Page: 147

View: 7015

Many scientific disciplines rely on observational data of systems for which it is difficult (or impossible) to implement controlled experiments. Data analysis techniques are required for identifying causal information and relationships directly from such observational data. This need has led to the development of many different time series causality approaches and tools including transfer entropy, convergent cross-mapping (CCM), and Granger causality statistics. A practicing analyst can explore the literature to find many proposals for identifying drivers and causal connections in time series data sets. Exploratory causal analysis (ECA) provides a framework for exploring potential causal structures in time series data sets and is characterized by a myopic goal to determine which data series from a given set of series might be seen as the primary driver. In this work, ECA is used on several synthetic and empirical data sets, and it is found that all of the tested time series causality tools agree with each other (and intuitive notions of causality) for many simple systems but can provide conflicting causal inferences for more complicated systems. It is proposed that such disagreements between different time series causality tools during ECA might provide deeper insight into the data than could be found otherwise.
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Explanation in Causal Inference

Methods for Mediation and Interaction

Author: Tyler VanderWeele

Publisher: Oxford University Press

ISBN: 019932588X

Category: Psychology

Page: 248

View: 6508

The book provides an accessible but comprehensive overview of methods for mediation and interaction. There has been considerable and rapid methodological development on mediation and moderation/interaction analysis within the causal-inference literature over the last ten years. Much of this material appears in a variety of specialized journals, and some of the papers are quite technical. There has also been considerable interest in these developments from empirical researchers in the social and biomedical sciences. However, much of the material is not currently in a format that is accessible to them. The book closes these gaps by providing an accessible, comprehensive, book-length coverage of mediation. The book begins with a comprehensive introduction to mediation analysis, including chapters on concepts for mediation, regression-based methods, sensitivity analysis, time-to-event outcomes, methods for multiple mediators, methods for time-varying mediation and longitudinal data, and relations between mediation and other concepts involving intermediates such as surrogates, principal stratification, instrumental variables, and Mendelian randomization. The second part of the book concerns interaction or "moderation," including concepts for interaction, statistical interaction, confounding and interaction, mechanistic interaction, bias analysis for interaction, interaction in genetic studies, and power and sample-size calculation for interaction. The final part of the book provides comprehensive discussion about the relationships between mediation and interaction and unites these concepts within a single framework. This final part also provides an introduction to spillover effects or social interaction, concluding with a discussion of social-network analyses. The book is written to be accessible to anyone with a basic knowledge of statistics. Comprehensive appendices provide more technical details for the interested reader. Applied empirical examples from a variety of fields are given throughout. Software implementation in SAS, Stata, SPSS, and R is provided. The book should be accessible to students and researchers who have completed a first-year graduate sequence in quantitative methods in one of the social- or biomedical-sciences disciplines. The book will only presuppose familiarity with linear and logistic regression, and could potentially be used as an advanced undergraduate book as well.
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Introduction to Epidemiology

Author: Brigham Young University Utah Ray M Merrill,Ray M. Merrill

Publisher: Jones & Bartlett Publishers

ISBN: 0763793825

Category: Medical

Page: 418

View: 1355

New Edition Available 8/17/2012 Introduction to Epidemiology, Fifth Edition is the ideal introductory text for the epidemiology student with minimal training in the biomedical sciences and statistics. With updated tables, figures, and examples throughout, the Fifth Edition is a thorough revision that offers an all new chapter covering areas of modern epidemiology such as environmental epidemiology, social epidemiology, and reproductive epidemiology. The chapters feature several new case studies and news files representing applications of commonly used research designs. Learning objectives, as well as study questions with descriptive answers, in each chapter engage the student in further analysis and reflection.
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Introduction to Epidemiology

Author: Brigham Young University Utah Ray M Merrill,Ray M. Merrill

Publisher: Jones & Bartlett Publishers

ISBN: 1449645186

Category: Education

Page: 425

View: 2385

Introduction to Epidemiology, Sixth Edition is the ideal introductory text for the epidemiology student with minimal training in the biomedical sciences and statistics. With updated tables, figures, and examples throughout, the Sixth Edition is a thorough revision that offers an all new, real world examples to help better illustrate elusive concepts. Learning objectives, as well as study questions with descriptive answers, in each chapter engage the student in further analysis and reflection. New Features of the Sixth Edition: Updated tables and figures. Addition of several real-world, practical examples. Added focus on setting-up contingency tables and calculating and interpreting appropriate measures of association. Greater detail and examples on conducting hypothesis testing. Greater detail and examples on calculating age-adjusting rates. Greater detail and examples on calculating years of potential life lost. Greater detail and examples on assessing clinical trials. Updated case studies.
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Bias and Causation

Models and Judgment for Valid Comparisons

Author: Dr. Herbert I. Weisberg

Publisher: John Wiley & Sons

ISBN: 9781118058206

Category: Mathematics

Page: 348

View: 6223

A one-of-a-kind resource on identifying and dealing with bias in statistical research on causal effects Do cell phones cause cancer? Can a new curriculum increase student achievement? Determining what the real causes of such problems are, and how powerful their effects may be, are central issues in research across various fields of study. Some researchers are highly skeptical of drawing causal conclusions except in tightly controlled randomized experiments, while others discount the threats posed by different sources of bias, even in less rigorous observational studies. Bias and Causation presents a complete treatment of the subject, organizing and clarifying the diverse types of biases into a conceptual framework. The book treats various sources of bias in comparative studies—both randomized and observational—and offers guidance on how they should be addressed by researchers. Utilizing a relatively simple mathematical approach, the author develops a theory of bias that outlines the essential nature of the problem and identifies the various sources of bias that are encountered in modern research. The book begins with an introduction to the study of causal inference and the related concepts and terminology. Next, an overview is provided of the methodological issues at the core of the difficulties posed by bias. Subsequent chapters explain the concepts of selection bias, confounding, intermediate causal factors, and information bias along with the distortion of a causal effect that can result when the exposure and/or the outcome is measured with error. The book concludes with a new classification of twenty general sources of bias and practical advice on how mathematical modeling and expert judgment can be combined to achieve the most credible causal conclusions. Throughout the book, examples from the fields of medicine, public policy, and education are incorporated into the presentation of various topics. In addition, six detailed case studies illustrate concrete examples of the significance of biases in everyday research. Requiring only a basic understanding of statistics and probability theory, Bias and Causation is an excellent supplement for courses on research methods and applied statistics at the upper-undergraduate and graduate level. It is also a valuable reference for practicing researchers and methodologists in various fields of study who work with statistical data. This book was selected as the 2011 Ziegel Prize Winner in Technometrics for the best book reviewed by the journal. It is also the winner of the 2010 PROSE Award for Mathematics from The American Publishers Awards for Professional and Scholarly Excellence
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Advances in Complex Data Modeling and Computational Methods in Statistics

Author: Anna Maria Paganoni,Piercesare Secchi

Publisher: Springer

ISBN: 3319111493

Category: Mathematics

Page: 209

View: 4119

The book is addressed to statisticians working at the forefront of the statistical analysis of complex and high dimensional data and offers a wide variety of statistical models, computer intensive methods and applications: network inference from the analysis of high dimensional data; new developments for bootstrapping complex data; regression analysis for measuring the downsize reputational risk; statistical methods for research on the human genome dynamics; inference in non-euclidean settings and for shape data; Bayesian methods for reliability and the analysis of complex data; methodological issues in using administrative data for clinical and epidemiological research; regression models with differential regularization; geostatistical methods for mobility analysis through mobile phone data exploration. This volume is the result of a careful selection among the contributions presented at the conference "S.Co.2013: Complex data modeling and computationally intensive methods for estimation and prediction" held at the Politecnico di Milano, 2013. All the papers published here have been rigorously peer-reviewed.
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