B1376
Title: Causal discovery from multivariate functional data
Authors: Yang Ni - Texas AM University (United States) [presenting]
Abstract: Discovering causal relationships using multivariate functional data has received a significant amount of attention very recently. A functional linear structural equation model is introduced for causal structure learning. To enhance interpretability, the 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. It is proven that the proposed model is causally identifiable under standard assumptions that are often made in the causal discovery literature. To carry out the inference of the model, a fully Bayesian framework is developed with suitable prior specifications and uncertainty quantification through posterior summaries. The superior performance of the method is illustrated over existing methods in terms of causal graph estimation through extensive simulation studies. The proposed method is also demonstrated using a brain EEG dataset.
