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