University of Chicago - Department of Statistics
Structural equation models are multivariate statistical models that are defined by specifying noisy functional relationships among random variables. This talk treats the classical case of linear relationships and additive Gaussian noise terms. Each linear structural equation model is associated with a graph and corresponds to a polynomially parametrized set of positive definite covariance matrices.
A basic problem is to determine which models are identifiable. In other words, the problem is to determine the graphs for which the polynomial parametrization map is injective, possibly only generically so. I will discuss recent results on this problem based on joint work with Jan Draisma, Rina Foygel and Seth Sullivant.