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...
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| מחברים אחרים: | , |
| פורמט: | Licensed eBooks |
| שפה: | אנגלית |
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Cambridge, Mass. :
MIT Press,
©2000.
©2000 |
| מהדורה: | 2nd ed. / |
| סדרה: | Adaptive computation and machine learning.
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| גישה מקוונת: | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=138589 |
תוכן הענינים:
- 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.