Causation, prediction, and search.
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. What assumptions and methods allow us to turn observations into causal knowledge, an...
| Auteur principal: | |
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| Autres auteurs: | , |
| Format: | Licensed eBooks |
| Langue: | anglais |
| Publié: |
Cambridge, Mass. :
MIT Press,
©2000.
©2000 |
| Édition: | 2nd ed. / |
| Collection: | Adaptive computation and machine learning.
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| Accès en ligne: | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=138589 |
Table des matières:
- 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.