Prem S. Puri Memorial Lecture

Estimation of a Directed Acyclic Gaussian Graph

Xiaotong Shen
John Black Johnston Distinguished Professor
Department of Statistics, University of Minnesota

Start Date and Time: Fri, 24 Apr 2015, 10:30 AM

End Date and Time: Fri, 24 Apr 2015, 11:30 AM

Venue: WTHR 320

Refreshments: 10:00 in HAAS 111

Abstract:

Directed acyclic graphs are widely used to describe, among interacting units, causal relations. Causal relations are estimated by reconstructing a directed acyclic graph's structure, presenting a great challenge when the unknown total ordering of a DAG needs to be estimated.

In such a situation, it remains unclear if a graph's structure is reconstructable in the absence of an identifiable likelihood with regard to graphs, and in facing super-exponentially many candidate graphs in the number of nodes. In this talk, I will introduce a global approach for observational data and interventional data, to identify all estimable causal directions and estimate model parameters. This approach uses constrained maximum likelihood with nonconvex constraints reinforcing the non-loop requirement to yield an estimated directed acyclic graph, where super-exponentially many constraints characterize the major challenge. Computational issues will be discussed in addition to some theoretical aspects. This work is joint with Y. Yuan, W. Pan and Z. Wang.

Last Updated: Aug 31, 2017 11:36 AM

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