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 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.