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.

机器消除学习需要删除遗忘数据的信息，同时保留剩余数据的必要信息。该研究通过因果分析提出一种名为 Causal Machine Unlearning (CaMU) 的新框架，通过干预剩余数据的信息，消除与遗忘数据相关的因果影响，并同时保留剩余数据的因果相关性。实证结果表明，CaMU 提高了剩余数据的性能，并有效减小了遗忘数据的影响。这项工作首次从因果性的新视角解释了深度模型消除学习任务，并提供了基于因果分析的解决方案，为未来深度模型消除学习的研究开辟了新的可能性。