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Department Seminar Series: Xiaotong Shen, Estimation of a directed acyclic Gaussian graph

Friday, September 19, 2014
12:00 AM
411 West Hall

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.

 

Speaker: