Tutorial slides on Graphical Models for Causal Inference
The slides used in a recent UAI tutorial on
“Graphical Models for Causal Inference” are now
available for public view and public use.
click on
http://ftp.cs.ucla.edu/pub/stat_ser/uai12-mohan-pearl.pdf
The slides were prepared by Karthika Mohan
and the topics include:
1. probabilitic graphical models
2. Markov compatibility
3. d-separation
4. Interventions
5. Causal effects identification
6. do-Calculus
7. C-components
8. Counterfactuals
9. Markov Equivalence
10. MAGs
11. Confounding Equivalence
12. Instrumental Variables
13. Verma’s constraints
Enjoy
thank you for sharing this Tutorial slides on Graphical Models for Causal Inference
Comment by Husen Darmawan — May 22, 2015 @ 2:56 am