Causal Inference, Hernan, Miguel A.

Автор: B. Sreenivasulu et al.
Название: Causality Tests In Econometrics: Choice of Causal Variables
ISBN: 3659504041 ISBN-13(EAN): 9783659504044
Издательство: LAP LAMBERT Academic Publishing
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Цена: 49810.00 T
Наличие на складе: Нет в наличии.
Описание: In the Present Book Chapter-I is an introductory one.Chapter-II describes the concept and causal relations by econometric models. It presents the different representations such as autoregressive, Moving – average and univariate representation of causality. Chapter-III explore lucidly the various tests for causality, we come across in econometrics. In regression analysis, researchers are interested in testing for the exogenity of variables this testing is closely related to the causality test proposed by Granger, which is explained in detail in this chapter. Chapter-IV gives the conclusions about the present study.The various relevant research articles have been presented under the title BIBLIOGRAPHY.

Автор: VanderWeele Tyler
Название: Explanation in Causal Inference
ISBN: 0199325871 ISBN-13(EAN): 9780199325870
Издательство: Oxford Academ
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Цена: 121440.00 T
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Описание: 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.

Автор: Imbens
Название: Causal Inference for Statistics, Social, and Biomedical Sciences
ISBN: 0521885884 ISBN-13(EAN): 9780521885881
Издательство: Cambridge Academ
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Цена: 54910.00 T
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Описание: This text presents statistical methods for studying causal effects and discusses how readers can assess such effects in simple randomized experiments.

Автор: Morgan
Название: Counterfactuals and Causal Inference
ISBN: 1107694167 ISBN-13(EAN): 9781107694163
Издательство: Cambridge Academ
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Цена: 38010.00 T
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Описание: Cause-and-effect questions are the motivation for most research in the social, demographic, and health sciences. The counterfactual approach to causal analysis represents a unified framework for the prosecution of these questions. This second edition aims to convince more social scientists to take this approach when analyzing these core empirical questions.

Название: Statistical Methods for Dynamic Treatment Regimes
ISBN: 1461474272 ISBN-13(EAN): 9781461474272
Издательство: Springer
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Цена: 74530.00 T
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Описание: 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.

Автор: Mark J. van der Laan; Sherri Rose
Название: Targeted Learning
ISBN: 1461429110 ISBN-13(EAN): 9781461429111
Издательство: Springer
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Цена: 130430.00 T
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Описание: As the size of data sets grows ever larger, the need for valid statistical tools is greater than ever. This book introduces super learning and the targeted maximum likelihood estimator, and discusses complex data structures and related applied topics.

Автор: Jung
Название: An Investigation of the Causal Inference between Epidemiology and Jurisprudence
ISBN: 9811078610 ISBN-13(EAN): 9789811078613
Издательство: Springer
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Цена: 46570.00 T
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Описание: 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.

Автор: Pearl Judea
Название: Introduction to Causal Inference
ISBN: 1507894295 ISBN-13(EAN): 9781507894293
Издательство: Неизвестно
Цена: 11490.00 T
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Автор: Van Der Laan, Mark J. Rose, Sherri
Название: Targeted learning in data science
ISBN: 3030097366 ISBN-13(EAN): 9783030097363
Издательство: Springer
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Цена: 79190.00 T
Наличие на складе: Невозможна поставка.
Описание: This textbook for graduate students in statistics, data science, and public health deals with the practical challenges that come with big, complex, and dynamic data. It presents a scientific roadmap to translate real-world data science applications into formal statistical estimation problems by using the general template of targeted maximum likelihood estimators. These targeted machine learning algorithms estimate quantities of interest while still providing valid inference. Targeted learning methods within data science area critical component for solving scientific problems in the modern age. The techniques can answer complex questions including optimal rules for assigning treatment based on longitudinal data with time-dependent confounding, as well as other estimands in dependent data structures, such as networks. Included in Targeted Learning in Data Science are demonstrations with soft ware packages and real data sets that present a case that targeted learning is crucial for the next generation of statisticians and data scientists. Th is book is a sequel to the first textbook on machine learning for causal inference, Targeted Learning, published in 2011.Mark van der Laan, PhD, is Jiann-Ping Hsu/Karl E. Peace Professor of Biostatistics and Statistics at UC Berkeley. His research interests include statistical methods in genomics, survival analysis, censored data, machine learning, semiparametric models, causal inference, and targeted learning. Dr. van der Laan received the 2004 Mortimer Spiegelman Award, the 2005 Van Dantzig Award, the 2005 COPSS Snedecor Award, the 2005 COPSS Presidential Award, and has graduated over 40 PhD students in biostatistics and statistics.Sherri Rose, PhD, is Associate Professor of Health Care Policy (Biostatistics) at Harvard Medical School. Her work is centered on developing and integrating innovative statistical approaches to advance human health. Dr. Rose’s methodological research focuses on nonparametric machine learning for causal inference and prediction. She co-leads the Health Policy Data Science Lab and currently serves as an associate editor for the Journal of the American Statistical Association and Biostatistics.

Автор: Bibhas Chakraborty; Erica E.M. Moodie
Название: Statistical Methods for Dynamic Treatment Regimes
ISBN: 1489990305 ISBN-13(EAN): 9781489990303
Издательство: Springer
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Цена: 46570.00 T
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Описание: 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.

Автор: Brumback Babette A.
Название: Fundamentals of Causal Inference: With R
ISBN: 0367705052 ISBN-13(EAN): 9780367705053
Издательство: Taylor&Francis
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Цена: 64300.00 T
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Описание: 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.

Автор: Burgess Stephen, Thompson Simon G.
Название: Mendelian Randomization: Methods for Causal Inference Using Genetic Variants
ISBN: 0367341840 ISBN-13(EAN): 9780367341848
Издательство: Taylor&Francis
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Цена: 183750.00 T
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Описание: Mendelian randomization (MR) uses genetic instrumental variables to make inferences about causal effects based on observational data. It, therefore, can be a reliable way of assessing the causal nature of risk factors, such as biomarkers, for a wide range of disease outcomes.