\dm_csml_event_details UCL ELLIS

Rhino Deep Causal Temporal Relationship Learning with history-dependent noise


Nick Pawlowski & Wenbo Gong


Microsoft Research


Friday, 28 October 2022




Function Space, UCL Centre for Artificial Intelligence, 1st Floor, 90 High Holborn, London WC1V 6BH



Event series

DeepMind/ELLIS CSML Seminar Series


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 set the scene by covering 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.


Nick Pawlowski is a senior researcher at Microsoft Research Cambridge. His research interests include causality, variational inference and probabilistic reasoning and are currently focused on causal machine learningmethods aiming to improve decision making from observational data. Before join MSR, Nick completed his PhD at Imperial College London under the supervision from Ben Glocker. 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.