Continually Updating Neural Causal ModelsDownload PDF

Published: 11 Jan 2023, Last Modified: 05 May 2023AAAI23 Bridge Continual CausalityReaders: Everyone
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.
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