Computation Causation And Discovery

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Computation, Causation, and Discovery

Author : Clark N. Glymour
Publisher :
ISBN 13 :
Total Pages : 576 pages
Book Rating : 4.3/5 (91 download)

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Book Synopsis Computation, Causation, and Discovery by : Clark N. Glymour

Download or read book Computation, Causation, and Discovery written by Clark N. Glymour and published by . This book was released on 1999 with total page 576 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Author : Peter Spirtes
Publisher : Springer Science & Business Media
ISBN 13 : 1461227488
Total Pages : 551 pages
Book Rating : 4.4/5 (612 download)

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Book Synopsis Causation, Prediction, and Search by : Peter Spirtes

Download or read book Causation, Prediction, and Search written by Peter Spirtes and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 551 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Elements of Causal Inference

Author : Jonas Peters
Publisher : MIT Press
ISBN 13 : 0262037319
Total Pages : 289 pages
Book Rating : 4.2/5 (62 download)

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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 and published by MIT Press. This book was released on 2017-11-29 with total page 289 pages. 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.

Elements of Causal Inference

Author : Jonas Peters
Publisher : MIT Press
ISBN 13 : 0262344297
Total Pages : 289 pages
Book Rating : 4.2/5 (623 download)

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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 and published by MIT Press. This book was released on 2017-12-29 with total page 289 pages. 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.

Causation, Prediction, and Search

Author : Peter Spirtes
Publisher : MIT Press
ISBN 13 : 0262527928
Total Pages : 569 pages
Book Rating : 4.2/5 (625 download)

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Book Synopsis Causation, Prediction, and Search by : Peter Spirtes

Download or read book Causation, Prediction, and Search written by Peter Spirtes and published by MIT Press. This book was released on 2001-01-29 with total page 569 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Bayesian Nets and Causality: Philosophical and Computational Foundations

Author : Jon Williamson
Publisher : Oxford University Press
ISBN 13 : 019853079X
Total Pages : 250 pages
Book Rating : 4.1/5 (985 download)

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Book Synopsis Bayesian Nets and Causality: Philosophical and Computational Foundations by : Jon Williamson

Download or read book Bayesian Nets and Causality: Philosophical and Computational Foundations written by Jon Williamson and published by Oxford University Press. This book was released on 2005 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Innovations in Machine Learning

Author : Dawn E. Holmes
Publisher : Springer
ISBN 13 : 3540334866
Total Pages : 276 pages
Book Rating : 4.5/5 (43 download)

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Book Synopsis Innovations in Machine Learning by : Dawn E. Holmes

Download or read book Innovations in Machine Learning written by Dawn E. Holmes and published by Springer. This book was released on 2006-02-28 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning is currently one of the most rapidly growing areas of research in computer science. In compiling this volume we have brought together contributions from some of the most prestigious researchers in this field. This book covers the three main learning systems; symbolic learning, neural networks and genetic algorithms as well as providing a tutorial on learning casual influences. Each of the nine chapters is self-contained. Both theoreticians and application scientists/engineers in the broad area of artificial intelligence will find this volume valuable. It also provides a useful sourcebook for Postgraduate since it shows the direction of current research.

Discovering Causal Structure

Author : Clark Glymour
Publisher : Academic Press
ISBN 13 : 148326579X
Total Pages : 413 pages
Book Rating : 4.4/5 (832 download)

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Book Synopsis Discovering Causal Structure by : Clark Glymour

Download or read book Discovering Causal Structure written by Clark Glymour and published by Academic Press. This book was released on 2014-05-10 with total page 413 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Actual Causality

Author : Joseph Y. Halpern
Publisher : MIT Press
ISBN 13 : 0262537133
Total Pages : 240 pages
Book Rating : 4.2/5 (625 download)

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Book Synopsis Actual Causality by : Joseph Y. Halpern

Download or read book Actual Causality written by Joseph Y. Halpern and published by MIT Press. This book was released on 2019-02-19 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: A new approach for defining causality and such related notions as degree of responsibility, degrees of blame, and causal explanation. Causality plays a central role in the way people structure the world; we constantly seek causal explanations for our observations. But what does it even mean that an event C “actually caused” event E? The problem of defining actual causation goes beyond mere philosophical speculation. For example, in many legal arguments, it is precisely what needs to be established in order to determine responsibility. The philosophy literature has been struggling with the problem of defining causality since Hume. In this book, Joseph Halpern 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. Halpern applies and expands an approach to causality that he and Judea Pearl developed, based on structural equations. He carefully formulates a definition of causality, and building on this, defines degree of responsibility, degree of blame, and causal explanation. He concludes by discussing how these ideas can be applied to such practical problems as accountability and program verification. Technical details are generally confined to the final section of each chapter and can be skipped by non-mathematical readers.

Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing

Author : Guoyin Wang
Publisher : Springer Science & Business Media
ISBN 13 : 3540140409
Total Pages : 758 pages
Book Rating : 4.5/5 (41 download)

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Book Synopsis Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing by : Guoyin Wang

Download or read book Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing written by Guoyin Wang and published by Springer Science & Business Media. This book was released on 2003-05-08 with total page 758 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Emerging Trends in Computational Biology, Bioinformatics, and Systems Biology

Author : Hamid R Arabnia
Publisher : Morgan Kaufmann
ISBN 13 : 0128026464
Total Pages : 670 pages
Book Rating : 4.1/5 (28 download)

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Book Synopsis Emerging Trends in Computational Biology, Bioinformatics, and Systems Biology by : Hamid R Arabnia

Download or read book Emerging Trends in Computational Biology, Bioinformatics, and Systems Biology written by Hamid R Arabnia and published by Morgan Kaufmann. This book was released on 2015-08-11 with total page 670 pages. Available in PDF, EPUB and Kindle. Book excerpt: Emerging Trends in Computational Biology, Bioinformatics, and Systems Biology discusses the latest developments in all aspects of computational biology, bioinformatics, and systems biology and the application of data-analytics and algorithms, mathematical modeling, and simu- lation techniques. • Discusses the development and application of data-analytical and theoretical methods, mathematical modeling, and computational simulation techniques to the study of biological and behavioral systems, including applications in cancer research, computational intelligence and drug design, high-performance computing, and biology, as well as cloud and grid computing for the storage and access of big data sets. • Presents a systematic approach for storing, retrieving, organizing, and analyzing biological data using software tools with applications to general principles of DNA/RNA structure, bioinformatics and applications, genomes, protein structure, and modeling and classification, as well as microarray analysis. • Provides a systems biology perspective, including general guidelines and techniques for obtaining, integrating, and analyzing complex data sets from multiple experimental sources using computational tools and software. Topics covered include phenomics, genomics, epigenomics/epigenetics, metabolomics, cell cycle and checkpoint control, and systems biology and vaccination research. • Explains how to effectively harness the power of Big Data tools when data sets are so large and complex that it is difficult to process them using conventional database management systems or traditional data processing applications. Discusses the development and application of data-analytical and theoretical methods, mathematical modeling and computational simulation techniques to the study of biological and behavioral systems. Presents a systematic approach for storing, retrieving, organizing and analyzing biological data using software tools with applications. Provides a systems biology perspective including general guidelines and techniques for obtaining, integrating and analyzing complex data sets from multiple experimental sources using computational tools and software.

An Introduction to Causal Inference

Author : Judea Pearl
Publisher : Createspace Independent Publishing Platform
ISBN 13 : 9781507894293
Total Pages : 0 pages
Book Rating : 4.8/5 (942 download)

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Book Synopsis An Introduction to Causal Inference by : Judea Pearl

Download or read book An Introduction to Causal Inference written by Judea Pearl and published by Createspace Independent Publishing Platform. This book was released on 2015 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper summarizes recent advances in causal inference and underscores the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that underly all causal inferences, the languages used in formulating those assumptions, the conditional nature of all causal and counterfactual claims, and the methods that have been developed for the assessment of such claims. These advances are illustrated using a general theory of causation based on the Structural Causal Model (SCM) described in Pearl (2000a), which subsumes and unifies other approaches to causation, and provides a coherent mathematical foundation for the analysis of causes and counterfactuals. In particular, the paper surveys the development of mathematical tools for inferring (from a combination of data and assumptions) answers to three types of causal queries: (1) queries about the effects of potential interventions, (also called "causal effects" or "policy evaluation") (2) queries about probabilities of counterfactuals, (including assessment of "regret," "attribution" or "causes of effects") and (3) queries about direct and indirect effects (also known as "mediation"). Finally, the paper defines the formal and conceptual relationships between the structural and potential-outcome frameworks and presents tools for a symbiotic analysis that uses the strong features of both. The tools are demonstrated in the analyses of mediation, causes of effects, and probabilities of causation. -- p. 1.

Adaptive and Natural Computing Algorithms

Author : Bartlomiej Beliczynski
Publisher : Springer
ISBN 13 : 3540716181
Total Pages : 854 pages
Book Rating : 4.5/5 (47 download)

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Book Synopsis Adaptive and Natural Computing Algorithms by : Bartlomiej Beliczynski

Download or read book Adaptive and Natural Computing Algorithms written by Bartlomiej Beliczynski and published by Springer. This book was released on 2007-07-03 with total page 854 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two volume set constitutes the refereed proceedings of the 8th International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 2007, held in Warsaw, Poland, in April 2007. Coverage in the first volume includes evolutionary computation, genetic algorithms, and particle swarm optimization. The second volume covers neural networks, support vector machines, biomedical signal and image processing, biometrics, computer vision.

Information and Computation

Author : Gordana Dodig Crnkovic
Publisher : World Scientific
ISBN 13 : 9814295477
Total Pages : 530 pages
Book Rating : 4.8/5 (142 download)

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Book Synopsis Information and Computation by : Gordana Dodig Crnkovic

Download or read book Information and Computation written by Gordana Dodig Crnkovic and published by World Scientific. This book was released on 2011 with total page 530 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume provides a cutting-edge view of the world's leading authorities in fields where information and computation play a central role.

Causality, Probability, and Time

Author : Samantha Kleinberg
Publisher : Cambridge University Press
ISBN 13 : 1107026482
Total Pages : 269 pages
Book Rating : 4.1/5 (7 download)

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Book Synopsis Causality, Probability, and Time by : Samantha Kleinberg

Download or read book Causality, Probability, and Time written by Samantha Kleinberg and published by Cambridge University Press. This book was released on 2013 with total page 269 pages. Available in PDF, EPUB and Kindle. Book excerpt: Presents a new approach to causal inference and explanation, addressing both the timing and complexity of relationships.

The Cambridge Handbook of Computational Psychology

Author : Ron Sun
Publisher : Cambridge University Press
ISBN 13 : 0521674107
Total Pages : 767 pages
Book Rating : 4.5/5 (216 download)

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Book Synopsis The Cambridge Handbook of Computational Psychology by : Ron Sun

Download or read book The Cambridge Handbook of Computational Psychology written by Ron Sun and published by Cambridge University Press. This book was released on 2008-04-28 with total page 767 pages. Available in PDF, EPUB and Kindle. Book excerpt: A cutting-edge reference source for the interdisciplinary field of computational cognitive modeling.

Visions of Discovery

Author : Raymond Y. Chiao
Publisher : Cambridge University Press
ISBN 13 : 0521882397
Total Pages : 827 pages
Book Rating : 4.5/5 (218 download)

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Book Synopsis Visions of Discovery by : Raymond Y. Chiao

Download or read book Visions of Discovery written by Raymond Y. Chiao and published by Cambridge University Press. This book was released on 2011 with total page 827 pages. Available in PDF, EPUB and Kindle. Book excerpt: World-leading researchers, including Nobel Laureates, explore the most basic questions of science, philosophy, and the nature of existence.