Causal Inference

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Causal Inference


Causal Inference
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Causal Inference In Statistics


Causal Inference In Statistics
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Author : Judea Pearl
language : en
Publisher: John Wiley & Sons
Release Date : 2016-03-07

Causal Inference In Statistics written by Judea Pearl and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-03-07 with Mathematics categories.


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.



Causal Inference In Statistics Social And Biomedical Sciences


Causal Inference In Statistics Social And Biomedical Sciences
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Author : Guido W. Imbens
language : en
Publisher: Cambridge University Press
Release Date : 2015-04-06

Causal Inference In Statistics Social And Biomedical Sciences written by Guido W. Imbens and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-04-06 with Business & Economics categories.


This text presents statistical methods for studying causal effects and discusses how readers can assess such effects in simple randomized experiments.



Causal Inference


Causal Inference
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Author : Miquel A. Hernan
language : en
Publisher: CRC Press
Release Date : 2019-07-07

Causal Inference written by Miquel A. Hernan and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-07 with Medical categories.


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.



Counterfactuals And Causal Inference


Counterfactuals And Causal Inference
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Author : Stephen L. Morgan
language : en
Publisher: Cambridge University Press
Release Date : 2014-11-17

Counterfactuals And Causal Inference written by Stephen L. Morgan and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-11-17 with Social Science categories.


In this second edition of Counterfactuals and Causal Inference, completely revised and expanded, the essential features of the counterfactual approach to observational data analysis are presented with examples from the social, demographic, and health sciences. Alternative estimation techniques are first introduced using both the potential outcome model and causal graphs; after which, conditioning techniques, such as matching and regression, are presented from a potential outcomes perspective. For research scenarios in which important determinants of causal exposure are unobserved, alternative techniques, such as instrumental variable estimators, longitudinal methods, and estimation via causal mechanisms, are then presented. The importance of causal effect heterogeneity is stressed throughout the book, and the need for deep causal explanation via mechanisms is discussed.



Explanation In Causal Inference


Explanation In Causal Inference
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Author : Tyler J. VanderWeele
language : en
Publisher: Oxford University Press, USA
Release Date : 2015

Explanation In Causal Inference written by Tyler J. VanderWeele and has been published by Oxford University Press, USA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with Medical categories.


The book begins with a comprehensive introduction to mediation analysis, including chapters on concepts for mediation, regression-based methods, sensitivity analysis, time-to-event outcomes, methods for multiple mediators, methods for time-varying mediation and longitudinal data, and relations between mediation and other concepts involving intermediates such as surrogates, principal stratification, instrumental variables, and Mendelian randomization. The second part of the book concerns interaction or "moderation," including concepts for interaction, statistical interaction, confounding and interaction, mechanistic interaction, bias analysis for interaction, interaction in genetic studies, and power and sample-size calculation for interaction. The final part of the book provides comprehensive discussion about the relationships between mediation and interaction and unites these concepts within a single framework.



Statistical Models And Causal Inference


Statistical Models And Causal Inference
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Author : David A. Freedman
language : en
Publisher: Cambridge University Press
Release Date : 2010

Statistical Models And Causal Inference written by David A. Freedman and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with Mathematics categories.


David A. Freedman presents a definitive synthesis of his approach to statistical modeling and causal inference in the social sciences.



Elements Of Causal Inference


Elements Of Causal Inference
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Author : Jonas Peters
language : en
Publisher: MIT Press
Release Date : 2017-11-29

Elements Of Causal Inference written by Jonas Peters and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-29 with Computers categories.


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.