Statistical Approaches to Causal Analysis, Matthew McBee
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 provides the theoretical background to approach decision theory from a statistical perspective. It covers both traditional approaches, in terms of value theory and expected utility theory, and recent developments, in terms of causal inference.
Автор: Federica Russo Название: Causality and Causal Modelling in the Social Sciences ISBN: 9048179963 ISBN-13(EAN): 9789048179961 Издательство: Springer Рейтинг: Цена: 20962.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This investigation into causal modelling presents the rationale of causality; i.e. what guides reasoning in causal modeling. In contrast to the dominant paradigm, it argues that causal models are governed by a variation, rather than regularity or invariance.
Описание: This book compiles and presents new developments in statistical causal inference. The book brings together experts engaged in causal inference research to present and discuss recent issues in causal inference methodological development.
Описание: This book compiles and presents new developments in statistical causal inference. The accompanying data and computer programs are publicly available so readers may replicate the model development and data analysis presented in each chapter. In this way, methodology is taught so that readers may implement it directly. The book brings together experts engaged in causal inference research to present and discuss recent issues in causal inference methodological development. This is also a timely look at causal inference applied to scenarios that range from clinical trials to mediation and public health research more broadly. In an academic setting, this book will serve as a reference and guide to a course in causal inference at the graduate level (Master's or Doctorate). It is particularly relevant for students pursuing degrees in statistics, biostatistics, and computational biology. Researchers and data analysts in public health and biomedical research will also find this book to be an important reference.
Автор: Stephen L. Morgan Название: Handbook of Causal Analysis for Social Research ISBN: 9401794073 ISBN-13(EAN): 9789401794077 Издательство: Springer Рейтинг: Цена: 10480.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book reviews the tremendous progress of the past 15 years in the causal analysis of non-experimental data, also known as observational data, and shows how to use state-of-the-art methods for causal analysis in basic and applied empirical scholarship.
Автор: Mcbee, Matthew Название: Statistical approaches to causal analysis ISBN: 1526424738 ISBN-13(EAN): 9781526424730 Издательство: Sage Publications Рейтинг: Цена: 4275.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: A practical, up-to-date, step-by-step guidance on causal analysis; which features worked example datasets throughout to see methods in action. McBee clearly demonstrates techniques such as Rubin causal model, direct acyclic graphs and propensity score analysis.
Автор: Jiuyong Li; Lin Liu; Thuc Duy Le Название: Practical Approaches to Causal Relationship Exploration ISBN: 3319144324 ISBN-13(EAN): 9783319144320 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Introduction.- Local causal discovery with a simple PC algorithm.- A local causal discovery algorithm for high dimensional data.- Causal rule discovery with partial association test.- Causal rule discovery with cohort studies.- Experimental comparison and discussions.
Описание: It presents that potential effects of each independent variable on the dependent variable are not limited to direct and indirect effects. The path analysis shows each independent variable has a pure effect on the dependent variable. So, it can be shown the unique contribution of each independent variable to the variation of the dependent variable.
Автор: Vann Matthew T. Название: C-5 Channel Delays: Analysis of Potential Causal Factors ISBN: 128839568X ISBN-13(EAN): 9781288395682 Издательство: Неизвестно Рейтинг: Цена: 10658.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Discover best practices for real world data research with SAS code and examples
Real world health care data is common and growing in use with sources such as observational studies, patient registries, electronic medical record databases, insurance healthcare claims databases, as well as data from pragmatic trials. This data serves as the basis for the growing use of real world evidence in medical decision-making. However, the data itself is not evidence. Analytical methods must be used to turn real world data into valid and meaningful evidence. Real World Health Care Data Analysis: Causal Methods and Implementation Using SAS brings together best practices for causal comparative effectiveness analyses based on real world data in a single location and provides SAS code and examples to make the analyses relatively easy and efficient.
The book focuses on analytic methods adjusted for time-independent confounding, which are useful when comparing the effect of different potential interventions on some outcome of interest when there is no randomization. These methods include:
propensity score matching, stratification methods, weighting methods, regression methods, and approaches that combine and average across these methods
methods for comparing two interventions as well as comparisons between three or more interventions
algorithms for personalized medicine
sensitivity analyses for unmeasured confounding
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