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Statistical Causal Inferences and Their Applications in Public Health Research (ICSA Book Series in Statistics) de Hua He,Pan Wu,Ding-Geng (Din) Chen

Descripción - Críticas “This is an excellent overview of statistical causal inferences and their applications in public health research. This book is strongly recommended to students in statistics, biostatistics, and computational biology as well as to researchers in public health and biomedical research.” (Hemang B. Panchal, Doody's Book Reviews, April, 2017) Reseña del editor 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.  Contraportada 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. Biografía del autor Hua He, Ph.D., is an Associate Professor in Biostatistics at the Department of Epidemiology at Tulane University School of Public Health and Tropical Medicine. Dr. He received her Ph.D in Statistics in 2007 from the Department of Biostatistics and Computational Biology at the University of Rochester, where she then worked as a faculty member until she moved to Tulane University in 2015. Dr. He has been focusing on methodological and collaborative research with investigators in the areas of behavioral and social sciences both within and outside of academic institutes. She is a highly experienced biostatistician with expertise in longitudinal data analysis, structural equation models, potential outcome based causal inference, distribution-free models, ROC analysis and their applications to observational studies, and randomized controlled trials across a range of disciplines, especially in the behavioral and social sciences. She has published a series of publications in peer-reviewed journals and has contributed several chapters to books. She also co-authored a graduate-level textbook, Applied Categorical and Count Data Analysis (Chapman & Hall/CRC). She is the recipient of an R01 study entitled “Moving beyond description: statistical and causal inference for social media data” and has served as a co-investigator for multiple studies funded by NIH, NIMH, NHLBI, etc.Pan Wu, Ph.D., is a senior research biostatistician in the Value Institute at the Christiana Care Health System and a Research Assistant Professor in the Department of Medicine, the Sidney Kimmel Medical School at the Thomas Jefferson University. His research focuses on causal inference, mediation analysis, longitudinal data analysis with missing data, survival analysis, medical diagnosis, and high-dimensional variable selection and their applications in psychosocial, biomedical, and epidemiological studies. Dr. Wu has collaborated with a wide range of investigators on multiple research projects funded by NIH, NIMH, NHLBI, and AHRQ including mental health, cardiovascular disease, women’s health, and health optimization. He has published a series of important publications in development of new methodology in causal inference and applications in public health. One of the works on estimation of causal treatment effect for non-parametric statistics was published as a feature article in Statistics in Medicine in 2014. Dr. Wu got his Ph.D. in Statistics from the department of Biostatistics and Computational Biology at the University of Rochester in 2013. Ding-Geng Chen, Ph.D., is an elected Fellow of American Statistical Association for his leadership and influential contributions in biopharmaceutical statistics research; for leadership and prominent research contributions in public health; for major contributions to biostatistical methodology; for excellence in teaching and mentoring; and for prodigious and significant service to the statistical profession. He is currently the Wallace Kuralt distinguished professor at the University of North Carolina at Chapel Hill. He was a professor at the University of Rochester and the Karl E. Peace endowed eminent scholar chair in biostatistics at Georgia Southern University. He is also a senior statistics consultant for biopharmaceuticals and government agencies with extensive expertise in clinical trial biostatistics and public health statistics. Professor Chen has more than 100 referred professional publications and has co-authored and co-edited seven books on clinical trial methodology, meta-analysis, and public health applications. He has been invited nationally and internationally to give speeches on his research.

Detalles del Libro

• Name: Statistical Causal Inferences and Their Applications in Public Health Research (ICSA Book Series in Statistics)
  
• Autor: Hua He,Pan Wu,Ding-Geng (Din) Chen
  
• Categoria: Libros,Libros universitarios y de estudios superiores,Medicina y ciencias de la salud
  
• Tamaño del archivo: 10 MB
  
• Tipos de archivo: PDF Document
  
• Idioma: Español
  
• Archivos de estado: AVAILABLE
  

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Statistical Causal Inferences and Their Applications in ~ “This is an excellent overview of statistical causal inferences and their applications in public health research. This book is strongly recommended to students in statistics, biostatistics, and computational biology as well as to researchers in public health and biomedical research.” (Hemang B. Panchal, Doody's Book Reviews, April, 2017)

Statistical Causal Inferences and Their Applications in ~ Statistical Causal Inferences and Their Applications in Public Health Research (ICSA Book Series in Statistics) - Kindle edition by He, Hua, Wu, Pan, Chen, Ding-Geng (Din). Download it once and read it on your Kindle device, PC, phones or tablets. Use features like bookmarks, note taking and highlighting while reading Statistical Causal Inferences and Their Applications in Public Health .

Statistical Causal Inferences and Their Applications in ~ 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.

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Statistical Causal Inferences and Their Applications in ~ Statistical Causal Inferences and Their Applications in Public Health Research (ICSA Book Series in Statistics) （1st ed. 2016. 2016. xv, 321 S. 13 SW-Abb., 11 Farbabb. 235 mm） Wu, Pan / Herausgegeben von He, Hua / Chen, Ding-Geng Din

Statistical Causal Inferences and Their Applications in ~ Statistical Causal Inferences and Their Applications in Public Health Research Hua He , Pan Wu , Ding-Geng (Din) Chen (eds.) This book compiles and presents new developments in statistical causal inference.

Statistical Causal Inferences and Their Applications in ~ Statistical Causal Inferences and Their Applications in Public Health Research Statistics. Nov 03 2016 . . the comment form is closed at this time. Medical Translation Step by Step: Learning by Drafting Statistical Learning from a Regression Perspective, Second Edition: FOLLOW US ON TWITTER FOR LATEST UPDATES. STUDY MEDICAL PHOTOS.

Causal inference in statistics: An overview ~ J. Pearl/Causal inference in statistics 98. in the standard mathematicallanguageof statistics, and these extensions are not generally emphasized in the mainstream literature and education. As a result, large segments of the statistical research community find it hard to appreciate

Causal Inference Book - Harvard T.H. Chan School of Public ~ Causal Inference Book Jamie Robins and I have written a book that provides a cohesive presentation of concepts of, and methods for, causal inference. Much of this material is currently scattered across journals in several disciplines or confined to technical articles.

Causal Inference in Public Health / Annual Review of ~ Causal inference has a central role in public health; the determination that an association is causal indicates the possibility for intervention. We review and comment on the long-used guidelines for interpreting evidence as supporting a causal association and contrast them with the potential outcomes framework that encourages thinking in terms of causes that are interventions. We argue that .

Causal vs. Statistical Inference - Towards Data Science ~ The Book of Why. 2. Causality: Models, Reasoning and Inference. 3. Causal Inference in Statistics: A Primer. I personally think that the first one is good for a general audience since it also gives a good glimpse into the history of statistics and causality and then goes a bit more into the theory behind causal inference.

Statistical Causal Inferences and Their Applications in ~ Statistical Causal Inferences and Their Applications in Public Health Research / 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.

Causal Inference in Public Health ~ Causal inference has a central role in public health; the determination that an association is causal indicates the possibility for intervention. We review and comment on the long-used guidelines for interpreting evidence as supporting a causal association and contrast them with the potential outcomes framework that encourages thinking in terms of causes that are interventions.

Statistics and causal inference: A review / SpringerLink ~ This paper aims at assisting empirical researchers benefit from recent advances in causal inference. The paper stresses the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that underly all causal inferences, the languages used in formulating those assumptions, and .

Causal Inference, Causality, and Causal Statistics ~ Introduction to the Web site . Causal Statistics is a mathematical inquiring system which enables empirical researchers to draw causal inferences from non-experimental data, based upon the minimum required assumptions, explicitly stated. The non-experimental sciences (e.g., the social sciences, epidemiology, etc.) are and have, for well over a century, been in desperate need of a tool to make .

Causal Inference - an overview / ScienceDirect Topics ~ Kevin D. Hoover, in Philosophy of Economics, 2012. 5 Graph-Theoretic Accounts of Causal Structure. Causal inference using invariance testing is easily overwhelmed by too much happening at once. It works best when one or, at most, a few causal arrows are in question, and it requires (in economic applications, at least) the good fortune to have a few — but not too many — interventions in the .

Statistics and Causal Inference - Harvard University ~ Statistics and Causal Inference Kosuke Imai Princeton University February 2014 Academia Sinica, Taipei Kosuke Imai (Princeton) Statistics & Causal Inference Taipei (February 2014) 1 / 116

Statistics and Causal Inference - Harvard University ~ Causal effect of having a discussion leader with certain preferences on deliberation outcomes (Humphreys et al. 2006 WP) Causal effect of a job applicant’s gender/race on call-back rates (Bertrand and Mullainathan, 2004 AER) Kosuke Imai (Princeton) Statistics & Causal Inference EITM, June 2012 7 / 82

Randomization, Statistics, and Causal Inference : Epidemiology ~ This paper reviews the role of statistics in causal inference.Special attention is given to the need for randomization to justify causal inferences from conventional statistics, and the need for random sampling to justify descriptive inferences.In most epidemiologic studies, randomization and random sampling play little or no role in the assembly of study cohorts.

Causal Inference for Statistics, Social, and Biomedical ~ "Correctly drawing causal inferences is critical in many important applications. Congratulations to Professors Imbens and Rubin, who have drawn on their decades of research in this area, along with the work of several others, to produce this impressive book covering concepts, theory, methods and applications.

Statistics and Causal Inference - JSTOR ~ Statistics and Causal Inference PAUL W. HOLLAND* . show why the statistical models used to draw causal infer-ences are distinctly different from those used to draw as-sociational inferences. . Research Statistics Group, Educational Testing Service, Princeton, NJ 08541.

Causal Inference in Statistics: A Primer / Wiley ~ Many of the concepts and terminology surrounding modern causal inference can be quite intimidating to the novice. Judea Pearl presents a book ideal for beginners in statistics, providing a comprehensive introduction to the field of causality. Examples from classical statistics are presented throughout to demonstrate the need for causality in resolving decision-making dilemmas posed by data.

regression - What distinction is there between statistical ~ "Causal inference" mean reasoning about causation, whereas "statistical inference" means reasoning with statistics (it's more or less synonymous with the word "statistics" itself). So, causal inference is a subset of statistical inference, except that you can do some causal reasoning without statistics per se (e.g., if event A happened before event B, then B cannot have caused A).

Causal Inference for Statistics, Social, and Biomedical ~ 'Correctly drawing causal inferences is critical in many important applications. Congratulations to Professors Imbens and Rubin, who have drawn on their decades of research in this area, along with the work of several others, to produce this impressive book covering concepts, theory, methods and applications.

Causal Inference / Coursera ~ Offered by Columbia University. This course offers a rigorous mathematical survey of causal inference at the Master’s level. Inferences about causation are of great importance in science, medicine, policy, and business. This course provides an introduction to the statistical literature on causal inference that has emerged in the last 35-40 years and that has revolutionized the way in which .

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