  Recent neural-based aspect-based sentiment analysis approaches, though
achieving promising improvement on benchmark datasets, have reported suffering
from poor robustness when encountering confounder such as non-target aspects.
In this paper, we take a causal view to addressing this issue. We propose a
simple yet effective method, namely, Sentiment Adjustment (SENTA), by applying
a backdoor adjustment to disentangle those confounding factors. Experimental
results on the Aspect Robustness Test Set (ARTS) dataset demonstrate that our
approach improves the performance while maintaining accuracy in the original
test set.
