Dynamic Variational Autoencoders for Structured Causal Discovery in Time Series

This talk introduces a novel framework for learning dynamic causal relationships in multivariate time series using a Variational Autoencoder (VAE) architecture. The model integrates recurrent and probabilistic components to jointly capture temporal dependencies and causal structure, addressing limitations of traditional Granger-causality and constraint-based approaches.