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Statistics Seminar

Causal Discovery from Multivariate Functional Data by Yang Ni

Event Details

Date
Wednesday, October 16, 2024
Time
4-5 p.m.
Description

Abstract: Discovering causal relationship using multivariate functional data has received a significant amount of attention very recently. We introduce a functional linear structural equation model for causal structure learning. To enhance interpretability, our 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 prove that the proposed 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 illustrate the superior performance of our method over existing methods in terms of causal graph estimation through extensive simulation studies. We also demonstrate the proposed method using a brain EEG dataset.

Cost
Free

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