This short course covers recent developments in graphical and causal modeling in Statistics/Machine Learning. It is comprised of the following three lectures, each two hours long.

The course targets an audience with exposure to basic concepts in graphical and causal modeling (e.g., conditional independence, DAGs, d-separation, Markov equivalence, definition of causal effects/the do-operator). 


June 25, June 27, and July 2, 2024


Location  

Parkring 11, 2nd floor, Garching n. Munich

Seminar room: 8101.02.110 


Online

https://tum-conf.zoom-x.de/j/65826319465?pwd=LlopeTZ8YaHF18ezkNS5nqC7jTb3WY.1

Meeting-ID: 658 2631 9465 

Kenncode: 232588






Program


Tuesday, June 25, 2024

2 p.m. – 4 p.m.


Lecture 1: Learning from conditional independence when not all variables are measured: Ancestral graphs and the FCI algorithm.

Thursday, June 27, 2024

2 p.m. – 4 p.m.


Lecture 2: Identification of causal effects: A reformulation of the ID algorithm via the fixing operation.


Tuesday, July 2, 2024

2 p.m. – 4 p.m.


Lecture 3: Nested Markov models.



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