  Continual Learning for Named Entity Recognition (CL-NER) aims to learn a
growing number of entity types over time from a stream of data. However, simply
learning Other-Class in the same way as new entity types amplifies the
catastrophic forgetting and leads to a substantial performance drop. The main
cause behind this is that Other-Class samples usually contain old entity types,
and the old knowledge in these Other-Class samples is not preserved properly.
Thanks to the causal inference, we identify that the forgetting is caused by
the missing causal effect from the old data. To this end, we propose a unified
causal framework to retrieve the causality from both new entity types and
Other-Class. Furthermore, we apply curriculum learning to mitigate the impact
of label noise and introduce a self-adaptive weight for balancing the causal
effects between new entity types and Other-Class. Experimental results on three
benchmark datasets show that our method outperforms the state-of-the-art method
by a large margin. Moreover, our method can be combined with the existing
state-of-the-art methods to improve the performance in CL-NER
