Learning Causal Graphical Models with Latent Variables

author: Sam Maes, Université de Savoie

Categories

Top: Computer Science: Machine Learning: Graphical Models

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*Slides*
0:00	Learning Causal Graphical Models with Latent Variables
0:02	Introduction
0:30	Problem
2:26	Overview pt 3
2:51	Bayesian Networks (BN)
4:37	Causal Bayesian networks (CBN)
6:12	Modeling Latent Variables
7:11	Probabilistic vs Causal Inference
9:04	With latent variables
10:13	Overview pt 4
10:19	Our assumptions
12:53	Representation for causal inference
13:09	Modeling Latent Variables 1
13:18	Representation for causal inference 1
14:55	Inference in SMCMs
15:14	Representation for learning
16:26	Maximal Ancestral Graphs (MAG)
18:11	Learning from Observational Data
18:53	Markov Equivalence Class
20:07	Uncertainty in CPAGs
21:59	Inference in MAGs
22:17	Uncertainty in CPAGs 1
22:41	Inference in MAGs 1
23:13	Overview pt 5
23:15	CPAG - SMCM
23:43	CPAG - SMCM (Type 1)
24:21	Uncertainty in CPAGs 2
24:48	CPAG - SMCM (Type 1) 1
25:07	Uncertainty in CPAGs 3
25:52	CPAG - SMCM (Type 1) 2
26:44	CPAG - SMCM (Type 2)
26:52	CPAG - SMCM (ctd.)
28:29	Conclusion

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