Causation, prediction, and search.

Description

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.

Description

"What assumptions and methods allow us to turn observations in 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."--Jacket.

Subjects

Series Statement

• Adaptive computation and machine learning

Other Editions

• Causation, prediction, and search.MIT Press2000-01-01
• Causation, prediction, and searchSpringer-Verlag1993-01-01
• Causation, Prediction, and SearchMIT Press2000-01-01
• Causation, Prediction, and SearchMIT Press2001-01-01
• Causation, Prediction, and SearchMIT Press2001-01-01
• Causation, Prediction, and SearchpaperbackSpringer2011-09-26