Candidate
Ryan Kinnear
Title
Learning Granger Causality Graphs: Causal Inference for Time Series Data
Supervisor
Ravi Mazumdar
Abstract
Identifying causal (rather than merely correlative) relationships in dynamic systems is a difficult task, particularly if it is not feasible to perform controlled experiments. Granger's notion of causality was developed first in economics beginning in the 1960s and can be used to form a network of plausible causal relations given only the opportunity to observe the system. This method is applied, for example, in neuro-imaging to identify relationships amongst brain regions, and in biostatistics to explore gene regulatory networks. In this talk, we provide an overview of the notion of Granger Causality, some applications, and methods for learning Granger Causality Networks in practice.