causality statistical perspectives and applications

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Causality

Author : Carlo Berzuini
ISBN : 9781119941736
Genre : Mathematics
File Size : 70. 68 MB
Format : PDF
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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.

Applied Bayesian Modeling And Causal Inference From Incomplete Data Perspectives

Author : Donald B. Rubin
ISBN : 047009043X
Genre : Mathematics
File Size : 23. 91 MB
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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.

Causal Inference In Statistics Social And Biomedical Sciences

Author : Guido W. Imbens
ISBN : 9780521885881
Genre : Business & Economics
File Size : 64. 39 MB
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This text presents statistical methods for studying causal effects and discusses how readers can assess such effects in simple randomized experiments.

Unifying Causality And Psychology

Author : Gerald Young
ISBN : 9783319240947
Genre : Psychology
File Size : 82. 19 MB
Format : PDF
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This magistral treatise approaches the integration of psychology through the study of the multiple causes of normal and dysfunctional behavior. Causality is the focal point reviewed across disciplines. Using diverse models, the book approaches unifying psychology as an ongoing project that integrates genetics, experience, evolution, brain, development, change mechanisms, and so on. The book includes in its integration free will, epitomized as freedom in being. It pinpoints the role of the self in causality and the freedom we have in determining our own behavior. The book deals with disturbed behavior, as well, and tackles the DSM-5 approach to mental disorder and the etiology of psychopathology. Young examines all these topics with a critical eye, and gives many innovative ideas and models that will stimulate thinking on the topic of psychology and causality for decades to come. It is truly integrative and original. Among the topics covered: Models and systems of causality of behavior. Nature and nurture: evolution and complexities. Early adversity, fetal programming, and getting under the skin. Free will in psychotherapy: helping people believe. Causality in psychological injury and law: basics and critics. A Neo-Piagetian/Neo-Eriksonian 25-step (sub)stage model. Unifying Causality and Psychology appeals to the disciplines of psychology, psychiatry, epidemiology, philosophy, neuroscience, genetics, law, the social sciences and humanistic fields, in general, and other mental health fields. Its level of writing makes it appropriate for graduate courses, as well as researchers and practitioners.

Propensity Score Analysis

Author : Shenyang Guo
ISBN : 9781483322520
Genre : Social Science
File Size : 82. 18 MB
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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.

Counterfactuals And Causal Inference

Author : Stephen L. Morgan
ISBN : 9781107065079
Genre : Mathematics
File Size : 87. 3 MB
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This new edition aims to convince social scientists to take a counterfactual approach to the core questions of their fields.

Statistics For High Dimensional Data

Author : Peter Bühlmann
ISBN : 9783642201929
Genre : Mathematics
File Size : 76. 28 MB
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Modern statistics deals with large and complex data sets, and consequently with models containing a large number of parameters. This book presents a detailed account of recently developed approaches, including the Lasso and versions of it for various models, boosting methods, undirected graphical modeling, and procedures controlling false positive selections. A special characteristic of the book is that it contains comprehensive mathematical theory on high-dimensional statistics combined with methodology, algorithms and illustrations with real data examples. This in-depth approach highlights the methods’ great potential and practical applicability in a variety of settings. As such, it is a valuable resource for researchers, graduate students and experts in statistics, applied mathematics and computer science.

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