The field of graphical causal inference and relational deep learning is witnessing significant developments, with a focus on improving the efficiency and scalability of algorithms and models. Researchers are exploring new approaches to represent complex relationships between variables, such as higher-order graph databases and relational entity graphs, which enable end-to-end representation learning without traditional feature engineering. Another area of advancement is the incorporation of knowledge graph information into relation extraction models, leading to enhanced performance, particularly in cases with imbalanced training data. Furthermore, novel methods for closed information extraction, such as discriminative approaches, are demonstrating superior performance compared to state-of-the-art generative models. Noteworthy papers in this area include: Linear-Time Primitives for Algorithm Development in Graphical Causal Inference, which introduces a framework for efficient algorithmic primitives in graphical causal inference. Analyzing the Influence of Knowledge Graph Information on Relation Extraction, which examines the impact of incorporating knowledge graph information on relation extraction models. Relational Deep Learning: Challenges, Foundations and Next-Generation Architectures, which provides a comprehensive review of relational deep learning and its applications.