Speaker:
Professor Thomas S Richardson
Department of Statistics
University of Washington, Seattle

Time: January 24, 2025, 15:00, Reception to follow

Location: 35.01.06, Room 6, Floor -1, Building 35 on CSS campus

Abstract:
Graphical models based on directed acyclic graphs (DAGs), also known as Bayesian networks, have found application. This stems from their well understood Markov properties and intuitive causal interpretation under the assumption that there are no unmeasured common causes. However, it has also been known for more than 30 years that DAG models with hidden variables give rise to non-parametric (“Verma”) constraints that generalize conditional independence. The nested Markov model is a class of graphical models associated with acyclic graphs containing directed and bidirected edges that encode all of the non-parametric equality constraints implied by DAGs with latent variables. In this talk I will first review the problem of causal identification from DAGs in the presence of hidden variables. This motivates a `fixing’ operation that may be applied to graphs and associated distributions. This operation leads to a simple reformulation of the ID algorithm of Tian & Pearl. I will then show that the fixing operation may be used to define the nested Markov model and the associated global property. I will also describe simple preservation rules for reasoning with such constraints. Finally, (time permitting) I will outline a local property and sketch why this construction is more involved than for ordinary independence models.

[Joint work with Robin J. Evans (Oxford), James M. Robins (Harvard) and Ilya Shpitser (Johns Hopkins).]

Registration: There is no need to register
Zoom link: upon request only

If you have any questions, please contact Marie Krøger Pramming at marie.pramming@sund.ku.dk

Supported by DDSA and The SMARTbiomed Pioneer Centre.