用因果博弈提升多标签分类，尤其改善稀有标签预测。
Causal Invariance and Counterfactual Learning Driven Cooperative Game for Multi-Label Classification
- 将多标签分类建模为合作博弈，结合因果发现与反事实奖励
- 在多个数据集上显著提升稀有标签的预测准确率
- 适合需要鲁棒性与可解释性的多标签场景
多标签分类（MLC）仍易受标签不平衡、虚假相关和分布偏移影响，对稀有标签预测尤为不利。为此，我们提出因果合作博弈（CCG）框架，将多标签分类视为合作多玩家互动。CCG通过神经结构方程模型显式进行因果发现，并结合反事实好奇心奖励驱动鲁棒特征学习。此外，引入因果不变性损失以确保跨环境泛化，并设计专门策略增强稀有标签表现。大量基准测试表明，CCG在稀有标签预测和整体鲁棒性方面均显著优于强基线。通过严谨的消融实验与定性分析，验证了各组件的有效性与可解释性，凸显了因果推断与合作博弈理论协同推动多标签学习的潜力。
原文摘要 · Abstract (English)
Multi-label classification (MLC) remains vulnerable to label imbalance, spurious correlations, and distribution shifts, challenges that are particularly detrimental to rare label prediction. To address these limitations, we introduce the Causal Cooperative Game (CCG) framework, which conceptualizes MLC as a cooperative multi-player interaction. CCG unifies explicit causal discovery via Neural Structural Equation Models with a counterfactual curiosity reward to drive robust feature learning. Furthermore, it incorporates a causal invariance loss to ensure generalization across diverse environments, complemented by a specialized enhancement strategy for rare labels. Extensive benchmarking demonstrates that CCG substantially outperforms strong baselines in both rare label prediction and overall robustness. Through rigorous ablation studies and qualitative analysis, we validate the efficacy and interpretability of our components, underscoring the potential of synergizing causal inference with cooperative game theory for advancing multi-label learning.
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