Continually Updating Neural Causal Models

Florian Peter Busch, Jonas Seng, Moritz Willig, Matej Zečević

TL;DR: Using continual learning to keep causal models updated over time

Abstract: A common assumption in causal modelling is that the relations between variables are fixed mechanisms. But in reality, these mechanisms often change over time and new data might not fit the original model as well. But is it reasonable to regularly train new models or can we update a single model continually instead? We propose utilizing the field of continual learning to help keep causal models updated over time.