Abstract: Deep learning models often lack of interpretability in their feature representations due to their “black-box” nature.Although existing disentangled representation learning methods can enhance interpretability to some extent by identifying independent factors within the data,they usually neglect complex correlations and potential causal structures,which limits their applicability in critical domains such as autonomous driving and medical diagnosis,especially in scenarios that require understanding and intervention of causal relationships.To address the insufficient causal modeling in current disentangled representation learning,a disentanglement framework integrating sparse coding with causal inference is constructed.Under appropriate supervision,this framework leverages a causal inference mechanism to precisely model causal relationships within the data,thereby not only generating high-quality and structured representations but also enabling the modeling and intervention of potential causal mechanisms,which significantly improves the model’s adaptability and robustness in causal tasks.Meanwhile,the embedded convolutional sparse coding layer imposes sparsity constraints to effectively filter key representations highly relevant to causal structures,further enhancing the model’s sensitivity and expressive capacity for higher-order causal relationships.Experimental results demonstrate that the proposed framework performs excellently on both the Pendulum and CelebA datasets,achieving a sample efficiency of 98.65% on the Pendulum dataset and 99.55% on the CelebA dataset.Moreover,it outperforms existing methods in terms of causal intervention effectiveness and distribution robustness,confirming its superiority in complex causal scenarios.

Key words: Sparse coding, Causal inference, Disentangled representation learning, Sample efficiency, Distribution robustness