Abstract:

Discovering causal relationships between different variables from time series data has been a long-standing challenge for many domains. Given the complexity of real-world relationships and the nature of observation in discrete time, the causal discovery method needs to consider non-linear relations between variables, instantaneous effects and history dependent noise. However, previous works do not offer a solution addressing all these problems together.

In the first part of this talk, we will first recap the basic concepts of causality, together with an end-to-end deep learning based causal inference model called DECI. In the second part, we will present our solution towards addressing the aforementioned challenges in real-world time series data by extending DECI. We name it Rhino, which can model non-linear relationships with instantaneous effects while allowing the noise distribution to be modulated by historical observations.

Bio: Wenbo Gong is a researcher at Microsoft Research Cambridge. He is interested in causality, approximate inference and deep generative models. Currently, he focuses on developing causal models for time series data and improving the posterior inference over DAGs. Before joining Microsoft, he finished his PhD at University of Cambridge under supervision from Jose Miguel Hernandez Lobato.

Organizer: Ruibo Tu