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 Рейтинг: Цена: 7472.00 р. 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 Рейтинг: Цена: 19008.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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.
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 Рейтинг: Цена: 5702.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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.
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: Mark J. van der Laan; Sherri Rose Название: Targeted Learning ISBN: 1461429110 ISBN-13(EAN): 9781461429111 Издательство: Springer Рейтинг: Цена: 19564.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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.
Описание: This long awaited successor of the original Cook/Campbell Quasi-Experimentation: Design and Analysis Issues for Field Settings represents updates in the field over the last two decades. The book covers four major topics in field experimentation:
Описание: In human genetics, causal inference methods leverage large omics data sets and phenotypic information to decipher various cause-and-effect relationships in human health and disease (e.g., smoking and lung cancer). The focus of such work is typically on modifiable variables (e.g., behavior or environmental exposure) that impact disease onset, progression, and outcome. A better understanding of these variables can lead to interventions and therapeutics that have a desirable impact on public health. Written and edited by experts in the field, this collection from Cold Spring Harbor Perspectives in Medicine examines advances in causal inference approaches in human genetics and how they are being used to enhance our understanding of human development and disease. The contributors discuss family-based study designs for causal inference, including twin designs, adoption designs, and in vitro fertilization designs, that separate inherited factors from perinatal environmental exposures. They also review various types of Mendelian randomization--a population-based approach that is growing in utility and popularity--as well as their integration with family-based designs. The use of these approaches to investigate causal mechanisms in specific scenarios (e.g., maternal smoking during pregnancy and ADHD in offspring) is also covered. This volume is therefore an essential read for geneticists, epidemiologists, and all biomedical scientists and public health professionals dedicated to using genetic information to improve human health.
Автор: Pearl Judea Название: Introduction to Causal Inference ISBN: 1507894295 ISBN-13(EAN): 9781507894293 Издательство: Неизвестно Цена: 1723.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Bibhas Chakraborty; Erica E.M. Moodie Название: Statistical Methods for Dynamic Treatment Regimes ISBN: 1489990305 ISBN-13(EAN): 9781489990303 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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 Рейтинг: Цена: 9645.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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.
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.
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