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

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. 1. Introduction And Advertisement -- 2. Formal Preliminaries -- 3. Causation And Prediction: Axioms And Explications -- 4. Statistical Indistinguishability -- 5. Discovery Algorithms For Causally Sufficient Structures -- 6. Discovery Algorithms Without Causal Sufficiency -- 7. Prediction -- 8. Regression, Causation, And Prediction -- 9. The Design Of Empirical Studies -- 10. The Structure Of The Unobserved -- 11. Elaborating Linear Theories With Unmeasured Variables -- 12. Prequels And Sequels -- 13. Proofs Of Theorems. Includes Bibliographical References (p. [495]-529) And Index.