The Art of Causal Conjecture

Author: Glenn Shafer

Publisher: MIT Press

ISBN: 9780262193689

Category: Computers

Page: 511

View: 5832

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In The Art of Causal Conjecture, Glenn Shafer lays out a new mathematical and philosophical foundation for probability and uses it to explain concepts of causality used in statistics, artificial intelligence, and philosophy.The various disciplines that use causal reasoning differ in the relative weight they put on security and precision of knowledge as opposed to timeliness of action. The natural and social sciences seek high levels of certainty in the identification of causes and high levels of precision in the measurement of their effects. The practical sciences--medicine, business, engineering, and artificial intelligence--must act on causal conjectures based on more limited knowledge. Shafer's understanding of causality contributes to both of these uses of causal reasoning. His language for causal explanation can guide statistical investigation in the natural and social sciences, and it can also be used to formulate assumptions of causal uniformity needed for decision making in the practical sciences.Causal ideas permeate the use of probability and statistics in all branches of industry, commerce, government, and science. The Art of Causal Conjecture shows that causal ideas can be equally important in theory. It does not challenge the maxim that causation cannot be proven from statistics alone, but by bringing causal ideas into the foundations of probability, it allows causal conjectures to be more clearly quantified, debated, and confronted by statistical evidence.
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The Dynamics of Judicial Proof

Computation, Logic, and Common Sense

Author: Marilyn MacCrimmon,Peter Tillers

Publisher: Physica

ISBN: 3790817929

Category: Law

Page: 494

View: 9248

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Fact finding in judicial proceedings is a dynamic process. This collection of papers considers whether computational methods or other formal logical methods developed in disciplines such as artificial intelligence, decision theory, and probability theory can facilitate the study and management of dynamic evidentiary and inferential processes in litigation. The papers gathered here have several epicenters, including (i) the dynamics of judicial proof, (ii) the relationship between artificial intelligence or formal analysis and "common sense," (iii) the logic of factual inference, including (a) the relationship between causality and inference and (b) the relationship between language and factual inference, (iv) the logic of discovery, including the role of abduction and serendipity in the process of investigation and proof of factual matters, and (v) the relationship between decision and inference.
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Causal Models and Intelligent Data Management

Author: Alex Gammerman

Publisher: Springer Science & Business Media

ISBN: 3642586481

Category: Computers

Page: 185

View: 7946

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The need to electronically store, manipulate and analyze large-scale, high-dimensional data sets requires new computational methods. This book presents new intelligent data management methods and tools, including new results from the field of inference. Leading experts also map out future directions of intelligent data analysis. This book will be a valuable reference for researchers exploring the interdisciplinary area between statistics and computer science as well as for professionals applying advanced data analysis methods in industry.
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Bayesian Nets and Causality: Philosophical and Computational Foundations

Author: Jon Williamson

Publisher: Oxford University Press

ISBN: 019853079X

Category: Computers

Page: 239

View: 4543

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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.
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Causal Learning

Advances in Research and Theory

Author: N.A

Publisher: Academic Press

ISBN: 9780080863856

Category: Psychology

Page: 442

View: 3700

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The Psychology of Learning and Motivation publishes empirical and theoretical contributions in cognitive and experimental psychology, ranging from classical and instrumental conditions to complex learning and problem solving. This guest-edited special volume is devoted to current research and discussion on associative versus cognitive accounts of learning. Written by major investigators in the field, topics include all aspects of causal learning in an open forum in which different approaches are brought together. Up-to-date review of the literature Discusses recent controversies Presents major advances in understanding causal learning Synthesizes contrasting approaches Includes important empirical contributions Written by leading researchers in the field
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Probability and Finance

It's Only a Game!

Author: Glenn Shafer,Vladimir Vovk

Publisher: John Wiley & Sons

ISBN: 0471461717

Category: Business & Economics

Page: 440

View: 4123

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Provides a foundation for probability based on game theory ratherthan measure theory. A strong philosophical approach with practicalapplications. Presents in-depth coverage of classical probability theory aswell as new theory.
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Probability Theory

Philosophy, Recent History and Relations to Science

Author: Vincent F. Hendricks,Stig Andur Pedersen,Klaus Frovin Jørgensen

Publisher: Springer Science & Business Media

ISBN: 9780792369523

Category: Mathematics

Page: 183

View: 9372

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A collection of papers presented at the conference on Probability Theory - Philosophy, Recent History and Relations to Science, University of Roskilde, Denmark, September 16-18, 1998. Since the measure theoretical definition of probability was proposed by Kolmogorov, probability theory has developed into a mature mathematical theory. It is today a fruitful field of mathematics that has important applications in philosophy, science, engineering, and many other areas. The measure theoretical definition of probability and its axioms, however, are not without their problems; some of them even puzzled Kolmogorov. This book sheds light on some recent discussions of the problems in probability theory and their history, analysing their philosophical and mathematical significance, and the role pf mathematical probability theory in other sciences.
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Bias and Causation

Models and Judgment for Valid Comparisons

Author: Dr. Herbert I. Weisberg

Publisher: John Wiley & Sons

ISBN: 9781118058206

Category: Mathematics

Page: 348

View: 8613

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A one-of-a-kind resource on identifying and dealing with bias in statistical research on causal effects Do cell phones cause cancer? Can a new curriculum increase student achievement? Determining what the real causes of such problems are, and how powerful their effects may be, are central issues in research across various fields of study. Some researchers are highly skeptical of drawing causal conclusions except in tightly controlled randomized experiments, while others discount the threats posed by different sources of bias, even in less rigorous observational studies. Bias and Causation presents a complete treatment of the subject, organizing and clarifying the diverse types of biases into a conceptual framework. The book treats various sources of bias in comparative studies—both randomized and observational—and offers guidance on how they should be addressed by researchers. Utilizing a relatively simple mathematical approach, the author develops a theory of bias that outlines the essential nature of the problem and identifies the various sources of bias that are encountered in modern research. The book begins with an introduction to the study of causal inference and the related concepts and terminology. Next, an overview is provided of the methodological issues at the core of the difficulties posed by bias. Subsequent chapters explain the concepts of selection bias, confounding, intermediate causal factors, and information bias along with the distortion of a causal effect that can result when the exposure and/or the outcome is measured with error. The book concludes with a new classification of twenty general sources of bias and practical advice on how mathematical modeling and expert judgment can be combined to achieve the most credible causal conclusions. Throughout the book, examples from the fields of medicine, public policy, and education are incorporated into the presentation of various topics. In addition, six detailed case studies illustrate concrete examples of the significance of biases in everyday research. Requiring only a basic understanding of statistics and probability theory, Bias and Causation is an excellent supplement for courses on research methods and applied statistics at the upper-undergraduate and graduate level. It is also a valuable reference for practicing researchers and methodologists in various fields of study who work with statistical data. This book was selected as the 2011 Ziegel Prize Winner in Technometrics for the best book reviewed by the journal. It is also the winner of the 2010 PROSE Award for Mathematics from The American Publishers Awards for Professional and Scholarly Excellence
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