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

Kelly, Kevin

Glymour, Clark

Spirtes, Peter

Scheines, Richard

Computers & Technology

Academic Pr

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

Bradford Books

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.

Causation, Prediction, and Search (Adaptive Computation and Machine Learning series)

MIT Press]

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

See the best price to sell, buy, or rent books by Spirtes, Peter. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Books by Spirtes, Peter