Stat Cafe - Saptarshi Roy

less than 1 minute read

Time: Wednesday 11/08/2023 from 11:30 AM to 12:20 PM
Location: BLOC 448
Pizza and drinks provided

Topic

Directed Cyclic Graph for Causal Discovery from Multivariate Functional Data

Abstract

Discovering causal relationship using multivariate functional data has received a significant amount of attention very recently. In this talk, we will introduce a functional linear structural equation model for causal structure learning when the underlying graph involving the multivariate functions may have cycles/feedback-loops. To enhance interpretability, the proposed model involves a low-dimensional causal embedded space such that all the relevant causal information in the multivariate functional data is preserved in this lower-dimensional subspace. We have proved that the model is causally identifiable under standard assumptions that are often made in the causal discovery literature. To carry out inference of our model, we develop a fully Bayesian framework with suitable prior specifications and uncertainty quantification through posterior summaries. We will illustrate the superior performance of our method over existing methods in terms of causal graph estimation through extensive simulation studies. A real data application using a brain EEG dataset will also be shown.