Computation Causation And Discovery
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Computation Causation And Discovery
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Author : Clark N. Glymour
language : en
Publisher:
Release Date : 1999
Computation Causation And Discovery written by Clark N. Glymour and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999 with Causation categories.
Computation Causation And Discovery
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Author : Clark N. Glymour
language : en
Publisher:
Release Date : 1999
Computation Causation And Discovery written by Clark N. Glymour and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999 with Business & Economics categories.
In science, business, and policymaking -- anywhere data are used in prediction -- two sorts of problems requiring very different methods of analysis often arise. The first, problems of recognition and classification, concerns learning how to use some features of a system to accurately predict other features of that system. The second, problems of causal discovery, concerns learning how to predict those changes to some features of a system that will result if an intervention changes other features. This book is about the second -- much more difficult -- type of problem. Typical problems of causal discovery are: How will a change in commission rates affect the total sales of a company? How will a reduction in cigarette smoking among older smokers affect their life expectancy? How will a change in the formula a college uses to award scholarships affect its dropout rate? These sorts of changes are interventions that directly alter some features of the system and perhaps -- and this is the question -- indirectly alter others. The contributors discuss recent research and applications using Bayes nets or directed graphic representations, including representations of feedback or recursive systems. The book contains a thorough discussion of foundational issues, algorithms, proof techniques, and applications to economics, physics, biology, educational research, and other areas.
Causation Prediction And Search
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Author : Peter Spirtes
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Causation Prediction And Search written by Peter Spirtes and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Mathematics categories.
This book is intended for anyone, regardless of discipline, who is interested in the use of statistical methods to help obtain scientific explanations or to predict the outcomes of actions, experiments or policies. Much of G. Udny Yule's work illustrates a vision of statistics whose goal is to investigate when and how causal influences may be reliably inferred, and their comparative strengths estimated, from statistical samples. Yule's enterprise has been largely replaced by Ronald Fisher's conception, in which there is a fundamental cleavage between experimental and non experimental inquiry, and statistics is largely unable to aid in causal inference without randomized experimental trials. Every now and then members of the statistical community express misgivings about this turn of events, and, in our view, rightly so. Our work represents a return to something like Yule's conception of the enterprise of theoretical statistics and its potential practical benefits. If intellectual history in the 20th century had gone otherwise, there might have been a discipline to which our work belongs. As it happens, there is not. We develop material that belongs to statistics, to computer science, and to philosophy; the combination may not be entirely satisfactory for specialists in any of these subjects. We hope it is nonetheless satisfactory for its purpose.
Discovering Causal Structure
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Author : Clark Glymour
language : en
Publisher: Academic Press
Release Date : 2014-05-10
Discovering Causal Structure written by Clark Glymour and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-05-10 with Social Science categories.
Discovering Causal Structure: Artificial Intelligence, Philosophy of Science, and Statistical Modeling provides information pertinent to the fundamental aspects of a computer program called TETRAD. This book discusses the version of the TETRAD program, which is designed to assist in the search for causal explanations of statistical data. or alternative models. This text then examines the notion of applying artificial intelligence methods to problems of statistical model specification. Other chapters consider how the TETRAD program can help to find god alternative models where they exist, and how it can help detect the existence of important neglected variables. This book discusses as well the procedures for specifying a model or models to account for non-experimental or quasi-experimental data. The final chapter presents a description of the format of input files and a description of each command. This book is a valuable resource for social scientists and researchers.
Rough Sets Fuzzy Sets Data Mining And Granular Computing
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Author : Guoyin Wang
language : en
Publisher: Springer Science & Business Media
Release Date : 2003-05-08
Rough Sets Fuzzy Sets Data Mining And Granular Computing written by Guoyin Wang and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-05-08 with Computers categories.
This book constitutes the refereed proceedings of the 9th International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, RSFDGrC 2003, held in Chongqing, China in May 2003. The 39 revised full papers and 75 revised short papers presented together with 2 invited keynote papers and 11 invited plenary papers were carefully reviewed and selected from a total of 245 submissions. The papers are organized in topical sections on rough sets foundations and methods; fuzzy sets and systems; granular computing; neural networks and evolutionary computing; data mining, machine learning, and pattern recognition; logics and reasoning; multi-agent systems; and Web intelligence and intelligent systems.
Causation Prediction And Search
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Author : Peter Spirtes
language : en
Publisher: MIT Press
Release Date : 2001-01-29
Causation Prediction And Search written by Peter Spirtes and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001-01-29 with Computers categories.
The authors address the assumptions and methods that allow us to turn observations into causal knowledge, and use even incomplete causal knowledge in planning and prediction to influence and control our environment. What assumptions and methods allow us to turn observations into causal knowledge, and how can even incomplete causal knowledge be used in planning and prediction to influence and control our environment? In this book Peter Spirtes, Clark Glymour, and Richard Scheines address these questions using the formalism of Bayes networks, with results that have been applied in diverse areas of research in the social, behavioral, and physical sciences. The authors show that although experimental and observational study designs may not always permit the same inferences, they are subject to uniform principles. They axiomatize the connection between causal structure and probabilistic independence, explore several varieties of causal indistinguishability, formulate a theory of manipulation, and develop asymptotically reliable procedures for searching over equivalence classes of causal models, including models of categorical data and structural equation models with and without latent variables. The authors show that the relationship between causality and probability can also help to clarify such diverse topics in statistics as the comparative power of experimentation versus observation, Simpson's paradox, errors in regression models, retrospective versus prospective sampling, and variable selection. The second edition contains a new introduction and an extensive survey of advances and applications that have appeared since the first edition was published in 1993.
The Cambridge Handbook Of Computational Psychology
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Author : Ron Sun
language : en
Publisher: Cambridge University Press
Release Date : 2008-04-28
The Cambridge Handbook Of Computational Psychology written by Ron Sun 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 2008-04-28 with Computers categories.
A cutting-edge reference source for the interdisciplinary field of computational cognitive modeling.
Bayesian Nets And Causality Philosophical And Computational Foundations
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Author : Jon Williamson
language : en
Publisher: Oxford University Press
Release Date : 2005
Bayesian Nets And Causality Philosophical And Computational Foundations written by Jon Williamson and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with Computers categories.
Bayesian nets are used in artificial intelligence as a calculus for causal reasoning, enabling machines to make predictions perform diagnoses, take decisions and even to discover causal relationships. This book brings together how to automate reasoning in artificial intelligence, and the nature of causality and probability in philosophy.
The Cambridge Handbook Of Computational Cognitive Sciences
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Author : Ron Sun
language : en
Publisher: Cambridge University Press
Release Date : 2023-05-11
The Cambridge Handbook Of Computational Cognitive Sciences written by Ron Sun 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 2023-05-11 with Psychology categories.
The Cambridge Handbook of Computational Cognitive Sciences is a comprehensive reference for this rapidly developing and highly interdisciplinary field. Written with both newcomers and experts in mind, it provides an accessible introduction of paradigms, methodologies, approaches, and models, with ample detail and illustrated by examples. It should appeal to researchers and students working within the computational cognitive sciences, as well as those working in adjacent fields including philosophy, psychology, linguistics, anthropology, education, neuroscience, artificial intelligence, computer science, and more.
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.