Causal Inference with Weak Instruments PDF Download
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Author: Kristof Kühn
Publisher:
ISBN:
Category :
Languages : en
Pages : 40
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Book Description
Author: Kristof Kühn
Publisher:
ISBN:
Category :
Languages : en
Pages : 40
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Book Description
Author: Sheng Wang (Ph.D.)
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Mendelian randomization (MR) has been a popular method in genetic epidemiology to estimate the effect of an exposure on an outcome using genetic variants as instrumental variables (IV), with two-sample summary-data MR being the most popular. Unfortunately, genetic variants in MR studies are not always valid instrumental variables as they are often weakly associated with the exposure and may violate the exclusion restriction due to pleiotropy. The failure to satisfy the IV assumptions can bias effect estimates and inflate Type I errors in hypothesis testing. This dissertation introduces methods on robust estimates and test statistics in two-sample summary-data MR when the assumptions of valid instruments are violated. In Chapter 2, we propose test statistics that are robust under weak instrument asymptotics by extending the Anderson-Rubin, Kleibergen, and conditional-likelihood ratio tests in econometrics to two-sample summary-data MR. We also use the proposed Anderson-Rubin test to develop a point estimator and to detect invalid instruments. We conclude with simulation studies and an empirical study and show that the proposed tests control size and have better power than existing methods with weak instruments. In Chapter 3, we present a clustering approach in the two-sample summary-data MR framework to robustly estimate the causal effect when the exclusion restriction assumption is violated. We show in simulations that our proposed method leads to lower bias in a variety of scenarios and conclude with an empirical study. Chapter 4 summarizes the contributions of this dissertation to the existing literature and discusses several potential extensions to this dissertation.
Author: Scott Cunningham
Publisher: Yale University Press
ISBN: 0300255888
Category : Business & Economics
Languages : en
Pages : 585
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Book Description
An accessible, contemporary introduction to the methods for determining cause and effect in the Social Sciences “Causation versus correlation has been the basis of arguments—economic and otherwise—since the beginning of time. Causal Inference: The Mixtape uses legit real-world examples that I found genuinely thought-provoking. It’s rare that a book prompts readers to expand their outlook; this one did for me.”—Marvin Young (Young MC) Causal inference encompasses the tools that allow social scientists to determine what causes what. In a messy world, causal inference is what helps establish the causes and effects of the actions being studied—for example, the impact (or lack thereof) of increases in the minimum wage on employment, the effects of early childhood education on incarceration later in life, or the influence on economic growth of introducing malaria nets in developing regions. Scott Cunningham introduces students and practitioners to the methods necessary to arrive at meaningful answers to the questions of causation, using a range of modeling techniques and coding instructions for both the R and the Stata programming languages.
Author: Stephen Burgess
Publisher: CRC Press
ISBN: 146657318X
Category : Mathematics
Languages : en
Pages : 222
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Book Description
Presents the Terminology and Methods of Mendelian Randomization for Epidemiological StudiesMendelian randomization 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 disea
Author: Roland R. Ramsahai
Publisher:
ISBN:
Category : Instrumental variables (Statistics)
Languages : en
Pages : 318
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Book Description
Author: Guido W. Imbens
Publisher: Cambridge University Press
ISBN: 0521885884
Category : Business & Economics
Languages : en
Pages : 647
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Book Description
This text presents statistical methods for studying causal effects and discusses how readers can assess such effects in simple randomized experiments.
Author: Joshua D. Angrist
Publisher: Princeton University Press
ISBN: 0691120358
Category : Business & Economics
Languages : en
Pages : 392
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Book Description
In addition to econometric essentials, this book covers important new extensions as well as how to get standard errors right. The authors explain why fancier econometric techniques are typically unnecessary and even dangerous.
Author: Henning Best
Publisher: SAGE
ISBN: 1473908353
Category : Social Science
Languages : en
Pages : 425
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Book Description
′The editors of the new SAGE Handbook of Regression Analysis and Causal Inference have assembled a wide-ranging, high-quality, and timely collection of articles on topics of central importance to quantitative social research, many written by leaders in the field. Everyone engaged in statistical analysis of social-science data will find something of interest in this book.′ - John Fox, Professor, Department of Sociology, McMaster University ′The authors do a great job in explaining the various statistical methods in a clear and simple way - focussing on fundamental understanding, interpretation of results, and practical application - yet being precise in their exposition.′ - Ben Jann, Executive Director, Institute of Sociology, University of Bern ′Best and Wolf have put together a powerful collection, especially valuable in its separate discussions of uses for both cross-sectional and panel data analysis.′ -Tom Smith, Senior Fellow, NORC, University of Chicago Edited and written by a team of leading international social scientists, this Handbook provides a comprehensive introduction to multivariate methods. The Handbook focuses on regression analysis of cross-sectional and longitudinal data with an emphasis on causal analysis, thereby covering a large number of different techniques including selection models, complex samples, and regression discontinuities. Each Part starts with a non-mathematical introduction to the method covered in that section, giving readers a basic knowledge of the method’s logic, scope and unique features. Next, the mathematical and statistical basis of each method is presented along with advanced aspects. Using real-world data from the European Social Survey (ESS) and the Socio-Economic Panel (GSOEP), the book provides a comprehensive discussion of each method’s application, making this an ideal text for PhD students and researchers embarking on their own data analysis.
Author: Andrew Gelman
Publisher: Cambridge University Press
ISBN: 9780521686891
Category : Mathematics
Languages : en
Pages : 654
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Book Description
This book, first published in 2007, is for the applied researcher performing data analysis using linear and nonlinear regression and multilevel models.
Author: Stephen Burgess
Publisher: CRC Press
ISBN: 1000399559
Category : Mathematics
Languages : en
Pages : 240
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Book Description
Mendelian Randomization: Methods For Causal Inference Using Genetic Variants provides thorough coverage of the methods and practical elements of Mendelian randomization analysis. It brings together diverse aspects of Mendelian randomization from the fields of epidemiology, statistics, genetics, and bioinformatics. Through multiple examples, the first part of the book introduces the reader to the concept of Mendelian randomization, showing how to perform simple Mendelian randomization investigations and interpret the results. The second part of the book addresses specific methodological issues relevant to the practice of Mendelian randomization, including robust methods, weak instruments, multivariable methods, and power calculations. The authors present the theoretical aspects of these issues in an easy-to-understand way by using non-technical language. The last part of the book examines the potential for Mendelian randomization in the future, exploring both methodological and applied developments. Features Offers first-hand, in-depth guidance on Mendelian randomization from leaders in the field Makes the diverse aspects of Mendelian randomization understandable to newcomers Illustrates technical details using data from applied analyses Discusses possible future directions for research involving Mendelian randomization Software code is provided in the relevant chapters and is also available at the supplementary website This book gives epidemiologists, statisticians, geneticists, and bioinformaticians the foundation to understand how to use genetic variants as instrumental variables in observational data. New in Second Edition: The second edition of the book has been substantially re-written to reduce the amount of technical content, and emphasize practical consequences of theoretical issues. Extensive material on the use of two-sample Mendelian randomization and publicly-available summarized data has been added. The book now includes several real-world examples that show how Mendelian randomization can be used to address questions of disease aetiology, target validation, and drug development
