You are all cordially invited to the AMLab seminar on Tuesday February 7 at 16:00 in C3.163, where Thijs van Ommen will give a talk titled “Recognizing linear structural equation models from observational data”. Afterwards there are the usual drinks and snacks!
Abstract: In a linear structural equation model, each variable is a linear function of other variables plus noise, and some noise terms may be correlated. Such a model can be represented by a mixed graph, with directed edges for causal relations and bidirected edges for correlated noise terms. Our goal is to learn the graph structure from observational data. To do this, we need to consider what constraints a model imposes on the observed covariance matrix. Some of these constraint do not correspond to (conditional) independences, and are not well understood. In particular, it is not even clear how to tell, by looking at two graphs, whether they impose exactly the same constraints. I will describe my progress in mapping out these models and their constraints.