CCG : Rare-Label Prediction via Neural SEM –Driven Causal Game (2025)

Abstract: Multi-label classification (MLC) faces persistent challenges from label imbalance, spurious correlations, and distribution shifts, especially in rare label prediction. We propose the Causal Cooperative Game (CCG) framework, which models MLC as a multi-player cooperative process. CCG integrates explicit causal discovery via Neural Structural Equation Models, a counterfactual curiosity reward to guide robust feature learning, and a causal invariance loss to ensure generalization across environments, along with targeted rare label enhancement. Extensive experiments on benchmark datasets demonstrate that CCG significantly improves rare label prediction and overall robustness compared to strong baselines. Ablation and qualitative analyses further validate the effectiveness and interpretability of each component. Our work highlights the promise of combining causal inference and cooperative game theory for more robust and interpretable multi-label learning.

## introduction multi-label classification (mlc) @xcite @xcite @xcite is a key task in machine learning @xcite , widely applied in fields such as nlp. however, @xcite @xcite , real-world datasets often suffer from label imbalance, where rare labels have low representation in the training data @xcite . as a result, models tend to overlook these rare labels during training @xcite , impacting prediction performance and generalization. as task complexity increases, effectively handling rare labels and improving model performance on imbalanced data remain significant challenges @xcite . mainstream multi-label classification methods rely on the statistical correlations of labels, such as resampling or adjusting loss functions to enhance focus on rare labels @xcite . however, these methods generally assume that labels are independently and identically distributed, failing to capture complex causal relationships, especially dependencies between rare labels and other labels @xcite . existing methods are primarily based on surface co-occurrence information, making it difficult to identify spurious correlations (e.g., the co-occurrence of highfrequency and rare labels @xcite ). this leads to insufficient generalization in rare label prediction. for instance, when the co-occurrence of label a and label b is merely a surface statistical relationship rather than a causal one, the model might incorrectly use such relationships for prediction, resulting in inaccurate outcomes @xcite . therefore, in environments with distribution shifts, reducing the impact of spurious correlations and enhancing model robustness becomes a significant challenge in multi-label classification @xcite . furthermore, distribution shifts (e.g., inconsistencies between training and testing data distributions) further weaken the generalization ability of traditional models. specifically, in rare label prediction, models often overly rely on features of frequent labels, neglecting the uniqueness of rare labels, which leads to performance degradation @xcite . hence, constructing new methods that can capture causal relationships among labels and improve the prediction accuracy of rare labels has become a key research direction. to alleviate the rare label problem, many methods have proposed different strategies (de alvis and seneviratne, 2024; @xcite @xcite . some methods balance label frequency through resampling techniques @xcite or increase the training weight of rare labels by designing weighted loss functions @xcite @xcite . another set of methods @xcite enhances the prediction ability of rare labels via multi-task learning or label embedding. although these approaches have shown improvements, they still rely on surface statistical correlations and lack the modeling of potential causal relationships. in recent years, causal reasoning has gained attention in machine learning as a means to eliminate spurious correlations among labels and improve model robustness under distribution shifts. however, existing studies mainly focus on single-label causal modeling, and the exploration of causal relationships in multi-label tasks remains insufficient @xcite . the motivation of this study arises from the current shortcomings of multi-label classification methods in handling rare labels, particularly the insufficient utilization of causal relationships among labels, which causes models to be susceptible to spurious correlations @xcite . to address this challenge, we introduce the concept of causal reasoning. the goal of this research is to propose a novel causal cooperative game learning framework by modeling multi-label classification as a multi-player cooperative game process. in this framework, each player is responsible for a specific subset of labels and learns the real dependencies among labels through causal discovery methods. specifically, the main innovations of this study include: the rare label problem one of the biggest challenges in mlc is the rare label problem, especially when datasets exhibit severe label imbalance @xcite @xcite . traditional learning algorithms often struggle with rare label prediction due to their low frequency and insufficient training samples @xcite . to address this issue, researchers have proposed various solutions, including resampling techniques and weighted loss functions that increase the training weight of rare labels @xcite . additionally, multi-task learning and label embedding techniques have been explored to enhance rare label representation learning @xcite . however, most of these methods rely on surface-level statistical relationships and fail to model the underlying causal dependencies among labels. since causal relationships provide deeper insights into label interactions, ignoring them can lead to spurious correlations, ultimately reducing prediction accuracy. causal machine learning causal inference has recently attracted attention for reducing spurious correlations and improving robustness in machine learning @xcite @xcite . while effective in singlelabel tasks, its use in multi-label classification is still limited, with most existing work focusing only on simple label relationships. to address this, we propose a neural sem-based framework that builds dynamic causal graphs to better capture true label dependencies and improve rare label prediction, offering a novel approach by combining causal reasoning with cooperative game theory. ## method causal reasoning and cooperative game theory are applied to solve multi-label text classification challenges. we propose four innovations: causal structure modeling: using neural sem to construct label dependencies as a learnable causal graph @xcite @xcite ) @xmath0, e), capturing genuine dependencies via edges e @xmath1. counterfactual learning: designing a curiosity reward c k (x) based on counterfactual reasoning, guiding the model to focus on causal features by comparing counterfactual samples @xcite . invariance principle: introducing a causal invariancebased objective l inv to ensure stable feature extraction across environments. rare label enhancement: using dynamic weights w rare (ℓ) and a specialized loss function l rare to improve rare label prediction. these innovations advance multi-label classification and causal learning in nlp. subsequent chapters detail implementation, theoretical derivations, and experiments. ## experiments we conduct a series of experiments to comprehensively evaluate our proposed ccg framework, including: (1) comparative performance with baselines (4.1), (2) qualitative causal analysis and visualization (4.2), (3) analysis of the impact of player number (4.3), (4) ablation study (4.4), and (5) robustness to distribution shifts (4.5). unless otherwise specified, the number of players is set to @xmath2. detailed hyperparameter settings for each experiment are provided in the supplementary material (appendix a). ## conclusion this paper tackles key challenges in multi-label classification (mlc), particularly rare label prediction and spurious correlation mitigation, by introducing the causal cooperative game (ccg) framework. ccg reformulates mlc as a multi-player cooperative process, combining explicit causal discovery with neural sems, a counterfactual curiosity reward for robust feature learning, a causal invariance principle for stable predictions, and targeted rare label enhancement. extensive experiments on benchmark datasets show that ccg notably improves performance-especially for rare labels-and enhances robustness to distribution shifts. ablation studies confirm the importance of each component, while qualitative analysis demonstrates the interpretability of learned causal structures. this work points to a promising direction for building more robust, generalizable, and inter-pretable mlc systems through causal inference and cooperative game theory.