Causal Inference

2019-07-07 Medical

Author : Miquel A. Hernan
Release : 2019-07-07
Genre : Medical
Kind : eBook
Book Rating : 165/5 ( reviews)

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Book Synopsis Causal Inference by : Miquel A. Hernan

Download or read book Causal Inference written by Miquel A. Hernan. This book was released on 2019-07-07. Available in PDF, EPUB and Kindle. Book excerpt: The application of causal inference methods is growing exponentially in fields that deal with observational data. Written by pioneers in the field, this practical book presents an authoritative yet accessible overview of the methods and applications of causal inference. With a wide range of detailed, worked examples using real epidemiologic data as well as software for replicating the analyses, the text provides a thorough introduction to the basics of the theory for non-time-varying treatments and the generalization to complex longitudinal data.

Causal Inference

2021-01-26 Business & Economics

Author : Scott Cunningham
Release : 2021-01-26
Genre : Business & Economics
Kind : eBook
Book Rating : 888/5 ( reviews)

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Book Synopsis Causal Inference by : Scott Cunningham

Download or read book Causal Inference written by Scott Cunningham. This book was released on 2021-01-26. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Elements of Causal Inference

2017-11-29 Computers

Author : Jonas Peters
Release : 2017-11-29
Genre : Computers
Kind : eBook
Book Rating : 319/5 ( reviews)

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Book Synopsis Elements of Causal Inference by : Jonas Peters

Download or read book Elements of Causal Inference written by Jonas Peters. This book was released on 2017-11-29. Available in PDF, EPUB and Kindle. Book excerpt: A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning. The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-contained and concise introduction to causal models and how to learn them from data. After explaining the need for causal models and discussing some of the principles underlying causal inference, the book teaches readers how to use causal models: how to compute intervention distributions, how to infer causal models from observational and interventional data, and how causal ideas could be exploited for classical machine learning problems. All of these topics are discussed first in terms of two variables and then in the more general multivariate case. The bivariate case turns out to be a particularly hard problem for causal learning because there are no conditional independences as used by classical methods for solving multivariate cases. The authors consider analyzing statistical asymmetries between cause and effect to be highly instructive, and they report on their decade of intensive research into this problem. The book is accessible to readers with a background in machine learning or statistics, and can be used in graduate courses or as a reference for researchers. The text includes code snippets that can be copied and pasted, exercises, and an appendix with a summary of the most important technical concepts.

Explanation in Causal Inference

2015 Mathematics

Author : Tyler J. VanderWeele
Release : 2015
Genre : Mathematics
Kind : eBook
Book Rating : 871/5 ( reviews)

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Book Synopsis Explanation in Causal Inference by : Tyler J. VanderWeele

Download or read book Explanation in Causal Inference written by Tyler J. VanderWeele. This book was released on 2015. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive examination of methods for mediation and interaction, VanderWeele's book is the first to approach this topic from the perspective of causal inference. Numerous software tools are provided, and the text is both accessible and easy to read, with examples drawn from diverse fields. The result is an essential reference for anyone conducting empirical research in the biomedical or social sciences.

Fundamentals of Causal Inference

2021-11-10 Mathematics

Author : Babette A. Brumback
Release : 2021-11-10
Genre : Mathematics
Kind : eBook
Book Rating : 30X/5 ( reviews)

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Book Synopsis Fundamentals of Causal Inference by : Babette A. Brumback

Download or read book Fundamentals of Causal Inference written by Babette A. Brumback. This book was released on 2021-11-10. Available in PDF, EPUB and Kindle. Book excerpt: One of the primary motivations for clinical trials and observational studies of humans is to infer cause and effect. Disentangling causation from confounding is of utmost importance. Fundamentals of Causal Inference 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. It also covers effect-measure modification, precision variables, mediation analyses, and time-dependent confounding. Several real data examples, simulation studies, and analyses using R motivate the methods throughout. The book assumes familiarity with basic statistics and probability, regression, and R and is suitable for seniors or graduate students in statistics, biostatistics, and data science as well as PhD students in a wide variety of other disciplines, including epidemiology, pharmacy, the health sciences, education, and the social, economic, and behavioral sciences. Beginning with a brief history and a review of essential elements of probability and statistics, a unique feature of the book is its focus on real and simulated datasets with all binary variables to reduce complex methods down to their fundamentals. Calculus is not required, but a willingness to tackle mathematical notation, difficult concepts, and intricate logical arguments is essential. While many real data examples are included, the book also features the Double What-If Study, based on simulated data with known causal mechanisms, in the belief that the methods are best understood in circumstances where they are known to either succeed or fail. Datasets, R code, and solutions to odd-numbered exercises are available at www.routledge.com.

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