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Causal Inference in Statistics

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Release : 2016-01-25
Genre : Mathematics
Kind : eBook
Book Rating : 862/5 ( reviews)

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Book Synopsis Causal Inference in Statistics by : Judea Pearl

Download or read book Causal Inference in Statistics written by Judea Pearl. This book was released on 2016-01-25. Available in PDF, EPUB and Kindle. Book excerpt: CAUSAL INFERENCE IN STATISTICS A Primer Causality is central to the understanding and use of data. Without an understanding of cause–effect relationships, we cannot use data to answer questions as basic as "Does this treatment harm or help patients?" But though hundreds of introductory texts are available on statistical methods of data analysis, until now, no beginner-level book has been written about the exploding arsenal of methods that can tease causal information from data. Causal Inference in Statistics fills that gap. Using simple examples and plain language, the book lays out how to define causal parameters; the assumptions necessary to estimate causal parameters in a variety of situations; how to express those assumptions mathematically; whether those assumptions have testable implications; how to predict the effects of interventions; and how to reason counterfactually. These are the foundational tools that any student of statistics needs to acquire in order to use statistical methods to answer causal questions of interest. This book is accessible to anyone with an interest in interpreting data, from undergraduates, professors, researchers, or to the interested layperson. Examples are drawn from a wide variety of fields, including medicine, public policy, and law; a brief introduction to probability and statistics is provided for the uninitiated; and each chapter comes with study questions to reinforce the readers understanding.

Causality

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Release : 2009-09-14
Genre : Computers
Kind : eBook
Book Rating : 60X/5 ( reviews)

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Book Synopsis Causality by : Judea Pearl

Download or read book Causality written by Judea Pearl. This book was released on 2009-09-14. Available in PDF, EPUB and Kindle. Book excerpt: Causality offers the first comprehensive coverage of causal analysis in many sciences, including recent advances using graphical methods. Pearl presents a unified account of the probabilistic, manipulative, counterfactual and structural approaches to causation, and devises simple mathematical tools for analyzing the relationships between causal connections, statistical associations, actions and observations. The book will open the way for including causal analysis in the standard curriculum of statistics, artificial intelligence ...

Causal Inference in Statistics, Social, and Biomedical Sciences

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Release : 2015-04-06
Genre : Business & Economics
Kind : eBook
Book Rating : 884/5 ( reviews)

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Book Synopsis Causal Inference in Statistics, Social, and Biomedical Sciences by : Guido W. Imbens

Download or read book Causal Inference in Statistics, Social, and Biomedical Sciences written by Guido W. Imbens. This book was released on 2015-04-06. Available in PDF, EPUB and Kindle. Book excerpt: This text presents statistical methods for studying causal effects and discusses how readers can assess such effects in simple randomized experiments.

Fundamentals of Causal Inference

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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.

Causal Inference in Statistics

Download Causal Inference in Statistics PDF Online Free

Author :
Release : 2016-03-07
Genre : Mathematics
Kind : eBook
Book Rating : 846/5 ( reviews)

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Book Synopsis Causal Inference in Statistics by : Judea Pearl

Download or read book Causal Inference in Statistics written by Judea Pearl. This book was released on 2016-03-07. Available in PDF, EPUB and Kindle. Book excerpt: 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. Causal methods are also compared to traditional statistical methods, whilst questions are provided at the end of each section to aid student learning.

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