  Recommendation models are typically trained on observational user interaction
data, but the interactions between latent factors in users' decision-making
processes lead to complex and entangled data. Disentangling these latent
factors to uncover their underlying representation can improve the robustness,
interpretability, and controllability of recommendation models. This paper
introduces the Causal Disentangled Variational Auto-Encoder (CaD-VAE), a novel
approach for learning causal disentangled representations from interaction data
in recommender systems. The CaD-VAE method considers the causal relationships
between semantically related factors in real-world recommendation scenarios,
rather than enforcing independence as in existing disentanglement methods. The
approach utilizes structural causal models to generate causal representations
that describe the causal relationship between latent factors. The results
demonstrate that CaD-VAE outperforms existing methods, offering a promising
solution for disentangling complex user behavior data in recommendation
systems.
