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Causation, Prediction, and Search / Edition 2
     

Causation, Prediction, and Search / Edition 2

by Peter Spirtes, Clark Glymour, Richard Scheines
 

ISBN-10: 0262194406

ISBN-13: 9780262194402

Pub. Date: 01/08/2001

Publisher: MIT Press

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.

Overview

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.

Product Details

ISBN-13:
9780262194402
Publisher:
MIT Press
Publication date:
01/08/2001
Series:
Adaptive Computation and Machine Learning series
Edition description:
second edition
Pages:
568
Product dimensions:
7.00(w) x 9.00(h) x 1.50(d)
Age Range:
18 Years

Related Subjects

Table of Contents

1. Introduction and Advertisement.- 1.1 The Issue.- 1.2 Advertisements.- 1.2.1 Bayes Networks from the Data.- 1.2.2 Structural Equation Models from the Data.- 1.2.3 Selection of Regressors.- 1.2.4 Causal Inference without Experiment.- 1.2.5 The Structure of the Unobserved.- 1.3 Themes.- 2. Formal Preliminaries.- 2.1 Graphs.- 2.2 Probability.- 2.3 Graphs and Probability Distributions.- 2.3.1 Directed Acyclic Graphs.- 2.3.2 Directed Independence Graphs.- 2.3.3 Faithfulness.- 2.3.4 d-separation.- 2.3.5 Linear Structures.- 2.4 Undirected Independence Graphs.- 2.5 Deterministic and Pseudo-Indeterministic Systems.- 2.6 Background Notes.- 3. Causation and Prediction: Axioms and Explications.- 3.1 Conditionals.- 3.2 Causation.- 3.2.1 Direct vs. Indirect Causation.- 3.2.2 Events and Variables.- 3.2.3 Examples.- 3.2.4 Representing Causal Relations with Directed Graphs.- 3.3 Causality and Probability.- 3.3.1 Deterministic Causal Structures.- 3.3.2 Pseudo-Indeterministic and Indeterministic Causal Structures.- 3.4 The Axioms.- 3.4.1 The Causal Markov Condition.- 3.4.2 The Causal Minimality Condition.- 3.4.3 The Faithfulness Condition.- 3.5 Discussion of the Conditions.- 3.5.1 The Causal Markov and Minimality Conditions.- 3.5.2 Faithfulness and Simpson’s Paradox.- 3.6 Bayesian Interpretations.- 3.7 Consequences of The Axioms.- 3.7.1 d-Separation.- 3.7.2 The Manipulation Theorem.- 3.8 Determinism.- 3.9 Background Notes.- 4. Statistical Indistinguishability.- 4.1 Strong Statistical Indistinguishability.- 4.2 Faithful Indistinguishability.- 4.3 Weak Statistical Indistinguishability.- 4.4 Rigid Indistinguishability.- 4.5 The Linear Case.- 4.6 Redefining Variables.- 4.7 Background Notes.- 5. Discovery Algorithms for Causally Sufficient Structures.- 5.1 Discovery Problems.- 5.2 Search Strategies in Statistics.- 5.2.1 The Wrong Hypothesis Space.- 5.2.2 Computational and Statistical Limitations.- 5.2.3 Generating a Single Hypothesis.- 5.2.4 Other Approaches.- 5.2.5 Bayesian Methods.- 5.3 The Wermuth-Lauritzen Algorithm.- 5.4 New Algorithms.- 5.4.1 The SGS Algorithm.- 5.4.2 The PC Algorithm.- 5.4.3 The IG (Independence Graph) Algorithm.- 5.4.4 Variable Selection.- 5.4.5 Incorporating Background Knowledge.- 5.5 Statistical Decisions.- 5.6 Reliability and Probabilities of Error.- 5.7 Estimation.- 5.8 Examples and Applications.- 5.8.1 The Causes of Publishing Productivity.- 5.8.2 Education and Fertility.- 5.8.3 The Female Orgasm.- 5.8.4 The American Occupational Structure.- 5.8.5 The ALARM Network.- 5.8.6 Virginity.- 5.8.7 The Leading Crowd.- 5.8.8 Influences on College Plans.- 5.8.9 Abortion Opinions.- 5.8.10 Simulation Tests with Random Graphs.- 5.9 Conclusion.- 5.10 Background Notes.- 6. Discovery Algorithms without Causal Sufficiency.- 6.1 Introduction.- 6.2 The PC Algorithm and Latent Variables.- 6.3 Mistakes.- 6.4 Inducing Paths.- 6.5 Inducing Path Graphs.- 6.6 Partially Oriented Inducing Path Graphs.- 6.7 Algorithms for Causal Inference with Latent Common Causes.- 6.8 Theorems on Detectable Causal Influence.- 6.9 Non-Independence Constraints.- 6.10 Generalized Statistical Indistinguishability and Linearity.- 6.11 The Tetrad Representation Theorem.- 6.12 An Example: Math Marks and Causal Interpretation.- 6.13 Background Notes.- 7. Prediction.- 7.1 Introduction.- 7.2 Prediction Problems.- 7.3 Rubin-Holland-Pratt-Schlaifer Theory.- 7.4 Prediction with Causal Sufficiency.- 7.5 Prediction without Causal Sufficiency.- 7.6 Examples.- 7.7 Conclusion.- 7.8 Background Notes.- 8. Regression, Causation and Prediction.- 8.1 When Regression Fails to Measure Influence.- 8.2 A Solution and Its Application.- 8.2.1 Components of the Armed Forces Qualification Test.- 8.2.2 The Causes of Spartina Biomass.- 8.2.3 The Effects of Foreign Investment on Political Repression.- 8.2.4 More Simulation Studies.- 8.3 Error Probabilities for Specification Searches.- 8.4 Conclusion.- 9. The Design of Empirical Studies.- 9.1 Observational or Experimental Study?.- 9.2 Selecting Variables.- 9.3 Sampling.- 9.4 Ethical Issues in Experimental Design.- 9.4.1 The Kadane/Sedransk/Seidenfeld Design.- 9.4.2 Causal Reasoning in the Experimental Design.- 9.4.3 Towards Ethical Trials.- 9.5 An Example: Smoking and Lung Cancer.- 9.6 Appendix.- 10. The Structure of the Unobserved.- 10.1 Introduction.- 10.2 An Outline of the Algorithm.- 10.3 Finding Almost Pure Measurement Models.- 10.3.1 Intra-Construct Foursomes.- 10.3.2 Cross-Construct Foursomes.- 10.4 Facts about the Unobserved Determined by the Observed.- 10.5 Unifying the Pieces.- 10.6 Simulation Tests.- 10.7 Conclusion.- 11. Elaborating Linear Theories with Unmeasured Variables.- 11.1 Introduction.- 11.2 The Procedure.- 11.2.1 Scoring.- 11.2.2 Search.- 11.3 The LISREL and EQS Procedures.- 11.3.1 Input and Output.- 11.3.2 Scoring.- 11.3.3 The LISREL VI Search.- 11.3.4 The EQS Search.- 11.4 The Primary Study.- 11.4.1 The Design of Comparative Simulation Studies.- 11.4.2 Study Design.- 11.5 Results.- 11.6 Reliability and Informativeness.- 11.7 Using LISREL and EQS as Adjuncts to Search.- 11.8 Limitations of the TETRAD II Elaboration Search.- 11.9 Some Morals for Statistical Search.- 12. Open Problems.- 12.1 Feedback, Reciprocal Causation, and Cyclic Graphs.- 12.1.1 Mason’s Theorem.- 12.1.2 Time Series and Cyclic Graphs.- 12.1.3 The Markov Condition, Factorizability and Faithfulness.- 12.1.4 Discovery Procedures.- 12.2 Indistinguishability Relations.- 12.3 Time series and Granger Causality.- 12.4 Model Specification and Parameter Estimation from the Same Data Base.- 12.5 Conditional Independence Tests.- 13. Proofs of Theorems.- 13.1 Theorem 2.1.- 13.2 Theorem 3.1.- 13.3 Theorem 3.2.- 13.4 Theorem 3.3.- 13.5 Theorem 3.4.- 13.6 Theorem 3.5.- 13.7 Theorem 3.6 (Manipulation Theorem).- 13.8 Theorem 3.7.- 13.9 Theorem 4.1.- 13.10 Theorem 4.2.- 13.11 Theorem 4.3.- 13.12 Theorem 4.4.- 13.13 Theorem 4.5.- 13.14 Theorem 4.6.- 13.15 Theorem 5.1.- 13.16 Theorem 6.1.- 13.17 Theorem 6.2..- 13.18 Theorem 6.3.- 13.19 Theorem 6.4.- 13.20 Theorem 6.5.- 13.21 Theorem 6.6.- 13.22 Theorem 6.7.- 13.23 Theorem 6.8.- 13.24 Theorem 6.9.- 13.25 Theorem 6.10 (Tetrad Representation Theorem).- 13.26 Theorem 6.11.- 13.27 Theorem 7.1.- 13.28 Theorem 7.2.- 13.29 Theorem 7.3.- 13.30 Theorem 7.4.- 13.31 Theorem 7.5.- 13.32 Theorem 9.1.- 13.33 Theorem 9.2.- 13.34 Theorem 10.1.- 13.35 Theorem 10.2.- 13.36 Theorem 11.1.

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