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...
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | , |
| التنسيق: | Licensed eBooks |
| اللغة: | الإنجليزية |
| منشور في: |
Cambridge, Mass. :
MIT Press,
©2000.
©2000 |
| الطبعة: | 2nd ed. / |
| سلاسل: | Adaptive computation and machine learning.
|
| الوصول للمادة أونلاين: | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=138589 |
جدول المحتويات:
- 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.