Discovering Causal Structure: Artificial Intelligence, Philosophy of Science, and Statistical Modeling

Kelly, Kevin

Glymour, Clark

Spirtes, Peter

Scheines, Richard

Computers & Technology

Academic Pr

Thinking Things Through provides a broad, historical, and rigorous introduction to the logical tradition in philosophy and to its contemporary significance. The presentation is centered around three of the most fruitful issues in Western thought: What are proofs, and why do they provide knowledge? How can experience be used to gain knowledge or to alter beliefs in a rational way? What is the nature of mind and of mental events and mental states? In a clear and lively style, Glymour describes these key philosophical problems and traces attempts to solve them, from ancient Greece to the present. Thinking Things Through reveals the philosophical sources of modern work in logic, the theory of computation, Bayesian statistics, cognitive psychology, and artificial intelligence, and it connects these subjects with contemporary problems in epistemology and metaphysics. The text is full of examples and problems, and an instructor's manual is available. Clark Glymour is Alumni Professor of Philosophy at Carnegie-Mellon University and Adjunct Professor of History and Philosophy of Science at the University of Pittsburgh.

Thinking Things Through: An Introduction to Philosophical Issues and Achievements

Health, Fitness & Dieting

Psychology & Counseling

Bradford Books

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.

Causation, Prediction, and Search, Second Edition (Adaptive Computation and Machine Learning)

Computer Science

The second edition of a unique introductory text, offering an account of the logical tradition in philosophy and its influence on contemporary scientific disciplines.
Thinking Things Through offers a broad, historical, and rigorous introduction to the logical tradition in philosophy and its contemporary significance. It is unique among introductory philosophy texts in that it considers both the historical development and modern fruition of a few central questions. It traces the influence of philosophical ideas and arguments on modern logic, statistics, decision theory, computer science, cognitive science, and public policy. The text offers an account of the history of speculation and argument, and the development of theories of deductive and probabilistic reasoning. It considers whether and how new knowledge of the world is possible at all, investigates rational decision making and causality, explores the nature of mind, and considers ethical theories. Suggestions for reading, both historical and contemporary, accompany most chapters. 
This second edition includes four new chapters, on decision theory and causal relations, moral and political theories, “moral tools” such as game theory and voting theory, and ethical theories and their relation to real-world issues. Examples have been updated throughout, and some new material has been added. It is suitable for use in advanced undergraduate and beginning graduate classes in philosophy, and as an ancillary text for students in computer science and the natural sciences.

Thinking Things Through, second edition: An Introduction to Philosophical Issues and Achievements (Bradford Book)

Politics & Social Sciences

Philosophy

Thinking Things Through provides a broad, historical, and rigorous introduction to the logical tradition in philosophy and to its contemporary significance. The presentation is centered around three of the most fruitful issues in Western thought: What are proofs, and why do they provide knowledge? How can experience be used to gain knowledge or to alter beliefs in a rational way? What is the nature of mind and of mental events and mental states? In a clear and lively style, Glymour describes these key philosophical problems and traces attempts to solve them, from ancient Greece to the present.
 Thinking Things Through reveals the philosophical sources of modern work in logic, the theory of computation, Bayesian statistics, cognitive psychology, and artificial intelligence, and it connects these subjects with contemporary problems in epistemology and metaphysics. The text is full of examples and problems, and an instructor's manual is available.Clark Glymour is Alumni Professor of Philosophy at Carnegie-Mellon University and Adjunct Professor of History and Philosophy of Science at the University of Pittsburgh.

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.

Computation, Causation, and Discovery

Cooper, Gregory F.

Aaai Pr

What did the trial of Galileo share with the trial for fraud of the foremost investigator of the effects of lead exposure on children’s intelligence? In the title essay of this rollicking collection on science and education, Clark Glymour argues that fundamentally both were disputes over what methods are legitimate and authoritative. From testing the expertise of NASA scientists to discovering where software goes to die to turning educational research upside down, Glymour’s reports from the front lines of science and education read like a blend of Rachel Carson and Hunter S. Thompson. Contrarian and original, he criticizes the statistical arguments against Teach for America, argues for teaching the fallacies of Intelligent Design in high school science, places contemporary psychological research in a Platonic cave dug by Freud, and gives (and rejects) a fair argument for a self-interested, nationalist response to climate change. One of the creators of influential new statistical methods, Glymour has been involved in scientific investigations on such diverse topics as wildfire prediction, planetary science, genomics, climate studies, psychology, and educational research. Now he provides personal reports of the funny, the absurd, and the appalling in contemporary science and education. More bemused than indignant, Galileo in Pittsburgh is an ever-engaging call to rethink how we do science and how we teach it.

Galileo in Pittsburgh

Harvard University Press

Introduction to the Philosophy of Science

A reprint of the Prentice-Hall edition of 1992.
Prepared by nine distinguished philosophers and historians of science, this thoughtful reader represents a cooperative effort to provide an introduction to the philosophy of science focused on cultivating an understanding of both the workings of science and its historical and social context. Selections range from discussions of topics in general methodology to a sampling of foundational problems in various physical, biological, behavioral, and social sciences. Each chapter contains a list of suggested readings and study questions.

Earman, John

Salmon, Merrilee H.

Lennox, James G.

Hackett Publishing Company, Inc.

This thoroughly thought-provoking book is unorthodox in its claim that under appropriate assumptions causal structures may be inferred from non-experimental sample data. The authors adopt two axioms relating causal relationships to probability distributions. These axioms have only been explicitly suggested in the statistical literature over the last 15 years but have been implicitly assumed in a variety of statistical disciplines. On the basis of these axioms, the authors propose a number of computationally efficient search procedures that infer causal relationships from non-experimental sample data and background knowledge. They also deduce a variety of theorems concerning estimation, sampling, latent variable existence and structure, regression, indistinguishability relations, experimental design, prediction, Simpsons paradox, and other topics. For the most part, technical details have been placed in the book's last chapter, and so the main results will be accessible to any research worker (regardless of discipline) who is interested in statistical methods to help establish or refute causal claims.

Causation, Prediction, and Search (Lecture Notes in Statistics)

Science & Math

Mathematics

Springer

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