The field of causal discovery and representation learning is rapidly advancing, with a focus on developing innovative methods for identifying causal relationships and representing complex systems. Recent research has emphasized the importance of addressing redundancy and equivalence in causal discovery, as well as developing more generalizable and interpretable models. Notably, there is a growing interest in applying causal graph neural networks to real-world problems, such as healthcare and drug synergy prediction. These models have shown promise in learning invariant mechanisms and mitigating the impact of distribution shift and discrimination. Furthermore, theoretical guarantees for causal discovery on large random graphs have been established, providing a foundation for reliable and efficient causal structure learning. Overall, the field is moving towards more robust, scalable, and interpretable causal discovery methods. Noteworthy papers include: Influence-aware Causal Autoencoder Network, which proposes a novel framework for node importance ranking in complex networks. CausalDDS, which disentangles causal substructures for interpretable and generalizable drug synergy prediction.