  Machine unlearning requires removing the information of forgetting data while
keeping the necessary information of remaining data. Despite recent
advancements in this area, existing methodologies mainly focus on the effect of
removing forgetting data without considering the negative impact this can have
on the information of the remaining data, resulting in significant performance
degradation after data removal. Although some methods try to repair the
performance of remaining data after removal, the forgotten information can also
return after repair. Such an issue is due to the intricate intertwining of the
forgetting and remaining data. Without adequately differentiating the influence
of these two kinds of data on the model, existing algorithms take the risk of
either inadequate removal of the forgetting data or unnecessary loss of
valuable information from the remaining data. To address this shortcoming, the
present study undertakes a causal analysis of the unlearning and introduces a
novel framework termed Causal Machine Unlearning (CaMU). This framework adds
intervention on the information of remaining data to disentangle the causal
effects between forgetting data and remaining data. Then CaMU eliminates the
causal impact associated with forgetting data while concurrently preserving the
causal relevance of the remaining data. Comprehensive empirical results on
various datasets and models suggest that CaMU enhances performance on the
remaining data and effectively minimizes the influences of forgetting data.
Notably, this work is the first to interpret deep model unlearning tasks from a
new perspective of causality and provide a solution based on causal analysis,
which opens up new possibilities for future research in deep model unlearning.
