You are all cordially invited to the AMLab seminar on Tuesday September 5 at 16:00 in C3.163, where Patrick Forré will give a talk titled “Markov Properties for Probabilistic Graphical Models with Latent Confounding and Feedback Loops”. Afterwards there are the usual drinks and snacks!
Abstract: The elegance and simplicity of Bayesian Networks, i.e. probabilistic graphical models for directed acyclic graphs (DAGs), is rooted in the equivalence of several Markov properties like: the recursive factorization property (rFP) which allows for sparse parametrization, the directed global Markov (dGMP) property encoding all conditional independences or the structural equation property (SEP) which expresses the variables in functional relations.
But as soon as we allow the graphical structure to have feedback loops and/or latent confounders the mentioned equivalences break down. In this talk we will introduce a new graphical structure which allows to represent both latent confounding and feedback loops at once, show how to generalize the most important Markov properties to this case and demonstrate how these Markov properties are logically related to each other. Furthermore, we will indicate how this new layer of theory might be used for causal discovery algorithms in the presence of latent confounders, non-linear functional relations and feedback loops.