Counterfactual debiasing is often used to reduce reliance on identity-linked shortcuts in harmful language understanding. We show that the same intervention can be harmful when identity mentions are not shortcuts but label-defining evidence. In multi-task cyberbullying modeling, identity cues may be spurious for cyberbullying detection (CD) and sentiment analysis (SA), yet necessary for fine-grained target identification (TI). We propose task-aware routing, which sends CD and SA through a debiased representation while preserving the original representation for TI. We evaluate this approach on the English-translated BullyExplain benchmark, HateXplain, and an external Measuring Hate Speech diagnostic. Experiments show that task-aware routing improves TI, while auxiliary detection and sentiment performance remain largely unchanged where applicable. Ablation studies attribute this improvement to representation routing that preserves identity-bearing evidence for TI rather than to class-weighting choices, with reduced-architecture diagnostics suggesting that the effect is not specific to the graph-enhanced model. These results support a task-conditional view of counterfactual debiasing: the same identity cue can warrant suppression in one task and preservation in another.