Fundamentals of Causal Inference

With R

Author: BABETTE A. BRUMBACK

Publisher: CRC Press

ISBN:

Category:

Page: 250

View: 766

One of the primary motivations for clinical trials and observational studies of humans is to infer cause and effect. Disentangling causation from confounding is of utmost importance. Fundamentals of Causal Inference explains and relates different methods of confounding adjustment in terms of potential outcomes and graphical models, including standardization, difference-in-differences estimation, the front-door method, instrumental variables estimation, and propensity score methods. It also covers effect-measure modification, precision variables, mediation analyses, and time-dependent confounding. Several real data examples, simulation studies, and analyses using R motivate the methods throughout. The book assumes familiarity with basic statistics and probability, regression, and R and is suitable for seniors or graduate students in statistics, biostatistics, and data science as well as PhD students in a wide variety of other disciplines, including epidemiology, pharmacy, the health sciences, education, and the social, economic, and behavioral sciences. Beginning with a brief history and a review of essential elements of probability and statistics, a unique feature of the book is its focus on real and simulated datasets with all binary variables to reduce complex methods down to their fundamentals. Calculus is not required, but a willingness to tackle mathematical notation, difficult concepts, and intricate logical arguments is essential. While many real data examples are included, the book also features the Double What-If Study, based on simulated data with known causal mechanisms, in the belief that the methods are best understood in circumstances where they are known to either succeed or fail. Datasets, R code, and solutions to odd-numbered exercises are available at www.routledge.com.

Propensity Score Analysis

Fundamentals and Developments

Author: Wei Pan

Publisher: Guilford Publications

ISBN:

Category: Psychology

Page: 402

View: 896

This book is designed to help researchers better design and analyze observational data from quasi-experimental studies and improve the validity of research on causal claims. It provides clear guidance on the use of different propensity score analysis (PSA) methods, from the fundamentals to complex, cutting-edge techniques. Experts in the field introduce underlying concepts and current issues and review relevant software programs for PSA. The book addresses the steps in propensity score estimation, including the use of generalized boosted models, how to identify which matching methods work best with specific types of data, and the evaluation of balance results on key background covariates after matching. Also covered are applications of PSA with complex data, working with missing data, controlling for unobserved confounding, and the extension of PSA to prognostic score analysis for causal inference. User-friendly features include statistical program codes and application examples. Data and software code for the examples are available at the companion website (www.guilford.com/pan-materials).

Epidemiology by Design

A Causal Approach to the Health Sciences

Author: Daniel Westreich

Publisher: Oxford University Press

ISBN:

Category: Medical

Page: 288

View: 921

A (LONG OVERDUE) CAUSAL APPROACH TO INTRODUCTORY EPIDEMIOLOGY Epidemiology is recognized as the science of public health, evidence-based medicine, and comparative effectiveness research. Causal inference is the theoretical foundation underlying all of the above. No introduction to epidemiology is complete without extensive discussion of causal inference; what's missing is a textbook that takes such an approach. Epidemiology by Design takes a causal approach to the foundations of traditional introductory epidemiology. Through an organizing principle of study designs, it teaches epidemiology through modern causal inference approaches, including potential outcomes, counterfactuals, and causal identification conditions. Coverage in this textbook includes: · Introduction to measures of prevalence and incidence (survival curves, risks, rates, odds) and measures of contrast (differences, ratios); the fundamentals of causal inference; and principles of diagnostic testing, screening, and surveillance · Description of three key study designs through the lens of causal inference: randomized trials, prospective observational cohort studies, and case-control studies · Discussion of internal validity (within a sample), external validity, and population impact: the foundations of an epidemiologic approach to implementation science For first-year graduate students and advanced undergraduates in epidemiology and public health fields more broadly, Epidemiology by Design offers a rigorous foundation in epidemiologic methods and an introduction to methods and thinking in causal inference. This new textbook will serve as a foundation not just for further study of the field, but as a head start on where the field is going.

Foundations of Agnostic Statistics

Author: Peter M. Aronow

Publisher: Cambridge University Press

ISBN:

Category: Political Science

Page: 314

View: 773

Reflecting a sea change in how empirical research has been conducted over the past three decades, Foundations of Agnostic Statistics presents an innovative treatment of modern statistical theory for the social and health sciences. This book develops the fundamentals of what the authors call agnostic statistics, which considers what can be learned about the world without assuming that there exists a simple generative model that can be known to be true. Aronow and Miller provide the foundations for statistical inference for researchers unwilling to make assumptions beyond what they or their audience would find credible. Building from first principles, the book covers topics including estimation theory, regression, maximum likelihood, missing data, and causal inference. Using these principles, readers will be able to formally articulate their targets of inquiry, distinguish substantive assumptions from statistical assumptions, and ultimately engage in cutting-edge quantitative empirical research that contributes to human knowledge.

Fundamentals of Data Mining in Genomics and Proteomics

Author: Werner Dubitzky

Publisher: Springer Science & Business Media

ISBN:

Category: Science

Page: 281

View: 264

This book presents state-of-the-art analytical methods from statistics and data mining for the analysis of high-throughput data from genomics and proteomics. It adopts an approach focusing on concepts and applications and presents key analytical techniques for the analysis of genomics and proteomics data by detailing their underlying principles, merits and limitations.

Counterfactuals and Causal Inference

Author: Stephen L. Morgan

Publisher: Cambridge University Press

ISBN:

Category: Mathematics

Page: 524

View: 194

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

Foundations of Info-Metrics

Modeling and Inference with Imperfect Information

Author: Amos Golan

Publisher: Oxford University Press

ISBN:

Category: Business & Economics

Page: 496

View: 879

Info-metrics is the science of modeling, reasoning, and drawing inferences under conditions of noisy and insufficient information. It is at the intersection of information theory, statistical inference, and decision-making under uncertainty. It plays an important role in helping make informeddecisions even when there is inadequate or incomplete information because it provides a framework to process available information with minimal reliance on assumptions that cannot be validated.In this pioneering book, Amos Golan, a leader in info-metrics, focuses on unifying information processing, modeling and inference within a single constrained optimization framework. Foundations of Info-Metrics provides an overview of modeling and inference, rather than a problem specific model, andprogresses from the simple premise that information is often insufficient to provide a unique answer for decisions we wish to make. Each decision, or solution, is derived from the available input information along with a choice of inferential procedure. The book contains numerous multidisciplinary applications and case studies, which demonstrate the simplicity and generality of the framework in real world settings. Examples include initial diagnosis at an emergency room, optimal dose decisions, election forecasting, network and informationaggregation, weather pattern analyses, portfolio allocation, strategy inference for interacting entities, incorporation of prior information, option pricing, and modeling an interacting social system. Graphical representations illustrate how results can be visualized while exercises and problem setsfacilitate extensions. This book is this designed to be accessible for researchers, graduate students, and practitioners across the disciplines.

Fundamentals of Marketing Research

Author: Scott M. Smith

Publisher: SAGE

ISBN:

Category: Business & Economics

Page: 881

View: 193

This book covers the fundamentals of research, including all the basic elements of method, techniques and analysis. The presentation is from primarily a pragmatic and user-oriented perspective which aides the student to evaluate the research presented to them. It explores cutting-edge technologies and new horizons while assuring students have a thorough grasp of research fundamentals. It: contains a wealth of modern methods and techniques not found in competing texts; provides numerous illustrative cases at the end of each section; integrates international marketing research throughout instead of placing it in a separate chapter; has a full chapter devoted to the essential topic of online research.

Fundamentals of Nursing Research

Author: Dorothy Young Brockopp

Publisher: Jones & Bartlett Learning

ISBN:

Category: Medical

Page: 536

View: 780

This book is written to inspire enthusiasm among nursing students toward the research process. Concepts are presented in a unique worktext format, which makes it easier for students to understand and simplify the principles of research.

Case Studies and Causal Inference

An Integrative Framework

Author: Ingo Rohlfing

Publisher: Palgrave Macmillan

ISBN:

Category: Education

Page: 257

View: 665

A discussion of the case study method which develops an integrative framework for causal inference in small-n research. This framework is applied to research design tasks such as case selection and process tracing. The book presents the basics, state-of-the-art and arguments for improving the case study method and empirical small-n research.

Case Studies and Causal Inference

An Integrative Framework

Author: I. Rohlfing

Publisher: Springer

ISBN:

Category: Political Science

Page: 257

View: 301

A discussion of the case study method which develops an integrative framework for causal inference in small-n research. This framework is applied to research design tasks such as case selection and process tracing. The book presents the basics, state-of-the-art and arguments for improving the case study method and empirical small-n research.

Cause and Effect, Conditionals, Explanations

Author: Richard L Epstein

Publisher: Advanced Reasoning Forum

ISBN:

Category: Philosophy

Page: 204

View: 112

This series of books presents the fundamentals of reasoning well, in a style accessible to both students and scholars. The text of each essay presents a story, the main line of development of the ideas, while the footnotes and appendices place the research within a larger scholarly context. The essays overlap, forming a unified analysis of reasoning, yet each essay is designed so that it may be read independently of the others. The topic of this volume is the evaluation of reasoning about cause and effect, reasoning using conditionals, and reasoning that involves explanations. The essay "Reasoning about Cause and Effect" sets out a way to analyze whether there is cause and effect in terms of whether an inference from a claim describing the purported cause to a claim describing the purported effect satisfies specific conditions. Different notions of cause and effect correspond to placing different conditions on what counts as a good causal inference. An application of that method in "The Directedness of Emotions" leads to a clearer understanding of the issue whether every emotion need be directed at something. In the essay "Conditionals" various ways of analyzing reasoning with claims of the form "if . . . then . . ." are surveyed. Some of those uses are meant to be judged as inferences that are not necessarily valid, and conditions are given for when we can consider such inferences to be good. In "Explanations" verbal answers to a question why a claim is true are evaluated in terms of conditions placed on inferences from the explaining claims to the claim being explained. Recognizing that the direction of inference of such an explanation is the reverse of that for an argument with the very same claims is crucial in their evaluation. Explanations in terms of functions and goals are also investigated.

The SAGE Handbook of Regression Analysis and Causal Inference

Author: Henning Best

Publisher: SAGE

ISBN:

Category: Reference

Page: 424

View: 910

'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.

Dynamic Treatment Regimes

Statistical Methods for Precision Medicine

Author: Anastasios A. Tsiatis

Publisher: CRC Press

ISBN:

Category: Mathematics

Page: 602

View: 843

Dynamic Treatment Regimes: Statistical Methods for Precision Medicine provides a comprehensive introduction to statistical methodology for the evaluation and discovery of dynamic treatment regimes from data. Researchers and graduate students in statistics, data science, and related quantitative disciplines with a background in probability and statistical inference and popular statistical modeling techniques will be prepared for further study of this rapidly evolving field. A dynamic treatment regime is a set of sequential decision rules, each corresponding to a key decision point in a disease or disorder process, where each rule takes as input patient information and returns the treatment option he or she should receive. Thus, a treatment regime formalizes how a clinician synthesizes patient information and selects treatments in practice. Treatment regimes are of obvious relevance to precision medicine, which involves tailoring treatment selection to patient characteristics in an evidence-based way. Of critical importance to precision medicine is estimation of an optimal treatment regime, one that, if used to select treatments for the patient population, would lead to the most beneficial outcome on average. Key methods for estimation of an optimal treatment regime from data are motivated and described in detail. A dedicated companion website presents full accounts of application of the methods using a comprehensive R package developed by the authors. The authors’ website www.dtr-book.com includes updates, corrections, new papers, and links to useful websites.

Handbook of Early Childhood Special Education

Author: Brian Reichow

Publisher: Springer

ISBN:

Category: Psychology

Page: 594

View: 450

This handbook discusses early childhood special education (ECSE), with particular focus on evidence-based practices. Coverage spans core intervention areas in ECSE, such as literacy, motor skills, and social development as well as diverse contexts for services, including speech-language pathology, physical therapy, and pediatrics. Contributors offer strategies for planning, implementing, modifying, and adapting interventions to help young learners extend their benefits into the higher grades. Concluding chapters emphasize the importance of research in driving evidence-based practices (EBP). Topics featured in the Handbook include: Family-centered practices in early childhood intervention. The application of Response to Intervention (RtI) in young children with identified disabilities. Motor skills acquisition for young children with disabilities. Implementing evidence-based practices in ECSE classrooms. · Cultural, ethnic, and linguistic implications for ECSE. The Handbook of Early Childhood Special Education is a must-have resource for researchers, professors, upper-level undergraduate and graduate students, clinicians, and practitioners across such disciplines as child and school psychology, early childhood education, clinical social work, speech and physical therapy, developmental psychology, behavior therapy, and public health.

Fundamentals of Neuroscience and the Law

Square Peg, Round Hole

Author: Erica Beecher-Monas

Publisher: Cambridge Scholars Publishing

ISBN:

Category: Law

Page: 415

View: 426

What does neuroscience tell us about voluntary movement? Why is the definition of “volition” so different from that of the legal definition of “intent”? Why are courts dismissing medically accepted mental health diagnoses? How can we draft better laws that are more scientifically based? What can recent advances in neuroscience tell us about the way we apply the law? This volume provides groundbreaking insights into the areas of scientific evidence and the intersection of neuroscience and law, and is the product of a collaboration by two experts in their respective fields. It is a primer for all those interested in neurolaw.

Fundamentals of Ecotoxicology

The Science of Pollution, Fourth Edition

Author: Michael C. Newman

Publisher: CRC Press

ISBN:

Category: Science

Page: 680

View: 260

An integrated analysis exploring current and relevant concepts, Fundamentals of Ecotoxicology: The Science of Pollution, Fourth Edition extends the dialogue further from the previous editions and beyond conventional ecosystems. It explores landscape, regional, and biospheric topics, communicating core concepts with subjects ranging from molecular t

Evidence-Based Health Care Management

Multivariate Modeling Approaches

Author: Thomas T.H. Wan

Publisher: Springer Science & Business Media

ISBN:

Category: Business & Economics

Page: 233

View: 178

Evidence-Based Health Care Management introduces the principles and methods for drawing sound causal inferences in research on health services management. The emphasis is on the application of structural equation modeling techniques and other analytical methods to develop causal models in health care management. Topics include causality, theoretical model building, and model verification. Multivariate modeling approaches and their applications in health care management are illustrated. The primary goals of the book are to present advanced principles of health services management research and to familiarize students with the multivariate analytic methods and procedures now in use in scientific research on health care management. The hope is to help health care managers become better equipped to use causal modeling techniques for problem solving and decision making. Evidence-based knowledge is derived from scientific replication and verification of facts. Used consistently and appropriately, it enables a health care manager to improve organizational performance. Causal inference in health care management is a highly feasible approach to establishing evidence-based knowledge that can help navigate an organization to high performance. This book introduces the principles and methods for drawing causal inferences in research on health services management.