Causality Probability And Time

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Causality Probability And Time
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Author : Samantha Kleinberg
language : en
Publisher: Cambridge University Press
Release Date : 2013
Causality Probability And Time written by Samantha Kleinberg 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 2013 with Computers categories.
Presents a new approach to causal inference and explanation, addressing both the timing and complexity of relationships.
Causality Probability And Time
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Author : Samantha Kleinberg
language : en
Publisher:
Release Date : 2013
Causality Probability And Time written by Samantha Kleinberg and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with Computational complexity categories.
"This book presents a new approach to causal inference and explanation, addressing both the timing and complexity of relationships. The method's feasibility and success is demonstrated through theoretical and experimental case studies"--
Causality Probability And Medicine
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Author : Donald Gillies
language : en
Publisher: Routledge
Release Date : 2018-08-15
Causality Probability And Medicine written by Donald Gillies and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-15 with Philosophy categories.
Why is understanding causation so important in philosophy and the sciences? Should causation be defined in terms of probability? Whilst causation plays a major role in theories and concepts of medicine, little attempt has been made to connect causation and probability with medicine itself. Causality, Probability, and Medicine is one of the first books to apply philosophical reasoning about causality to important topics and debates in medicine. Donald Gillies provides a thorough introduction to and assessment of competing theories of causality in philosophy, including action-related theories, causality and mechanisms, and causality and probability. Throughout the book he applies them to important discoveries and theories within medicine, such as germ theory; tuberculosis and cholera; smoking and heart disease; the first ever randomized controlled trial designed to test the treatment of tuberculosis; the growing area of philosophy of evidence-based medicine; and philosophy of epidemiology. This book will be of great interest to students and researchers in philosophy of science and philosophy of medicine, as well as those working in medicine, nursing and related health disciplines where a working knowledge of causality and probability is required.
Time And Causality Across The Sciences
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Author : Samantha Kleinberg
language : en
Publisher: Cambridge University Press
Release Date : 2019-09-26
Time And Causality Across The Sciences written by Samantha Kleinberg 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 2019-09-26 with Computers categories.
Explores the critical role time plays in our understanding of causality, across psychology, biology, physics and the social sciences.
Causality
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Author : Judea Pearl
language : en
Publisher: Cambridge University Press
Release Date : 2009-09-14
Causality written by Judea Pearl 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 2009-09-14 with Computers categories.
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 ...
Causality
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Author : Carlo Berzuini
language : en
Publisher: John Wiley & Sons
Release Date : 2012-06-04
Causality written by Carlo Berzuini 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 2012-06-04 with Mathematics categories.
A state of the art volume on statistical causality Causality: Statistical Perspectives and Applications presents a wide-ranging collection of seminal contributions by renowned experts in the field, providing a thorough treatment of all aspects of statistical causality. It covers the various formalisms in current use, methods for applying them to specific problems, and the special requirements of a range of examples from medicine, biology and economics to political science. This book: Provides a clear account and comparison of formal languages, concepts and models for statistical causality. Addresses examples from medicine, biology, economics and political science to aid the reader's understanding. Is authored by leading experts in their field. Is written in an accessible style. Postgraduates, professional statisticians and researchers in academia and industry will benefit from this book.
The Book Of Why
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Author : Judea Pearl
language : en
Publisher: Penguin UK
Release Date : 2018-05-15
The Book Of Why written by Judea Pearl and has been published by Penguin UK this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-05-15 with Philosophy categories.
The hugely influential book on how the understanding of causality revolutionized science and the world, by the pioneer of artificial intelligence 'Wonderful ... illuminating and fun to read' Daniel Kahneman, Nobel Prize-winner and author of Thinking, Fast and Slow 'Correlation does not imply causation.' For decades, this mantra was invoked by scientists in order to avoid taking positions as to whether one thing caused another, such as smoking and cancer, or carbon dioxide and global warming. But today, that taboo is dead. The causal revolution, sparked by world-renowned computer scientist Judea Pearl and his colleagues, has cut through a century of confusion and placed cause and effect on a firm scientific basis. Now, Pearl and science journalist Dana Mackenzie explain causal thinking to general readers for the first time, showing how it allows us to explore the world that is and the worlds that could have been. It is the essence of human and artificial intelligence. And just as Pearl's discoveries have enabled machines to think better, The Book of Why explains how we too can think better. 'Pearl's accomplishments over the last 30 years have provided the theoretical basis for progress in artificial intelligence and have redefined the term "thinking machine"' Vint Cerf
Actual Causality
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Author : Joseph Y. Halpern
language : en
Publisher: MIT Press
Release Date : 2016-08-12
Actual Causality written by Joseph Y. Halpern and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-08-12 with Computers categories.
Explores actual causality, and such related notions as degree of responsibility, degree of blame, and causal explanation. The goal is to arrive at a definition of causality that matches our natural language usage and is helpful, for example, to a jury deciding a legal case, a programmer looking for the line of code that cause some software to fail, or an economist trying to determine whether austerity caused a subsequent depression.
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.
Causal Inference In Statistics
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Author : Judea Pearl
language : en
Publisher: John Wiley & Sons
Release Date : 2016-01-25
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 2016-01-25 with Mathematics categories.
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.