The recent advancements in causal discovery and inference have significantly pushed the boundaries of traditional methods, particularly in the context of dynamic Bayesian networks and normalizing flows. A notable shift is the adoption of federated learning techniques to address the challenges of data decentralization and privacy preservation, enabling collaborative structure learning without compromising security. This approach has demonstrated superior performance, especially in scenarios with limited individual sample sizes. Additionally, the integration of causal consistency into generative models, such as normalizing flows, has led to the development of more robust and expressive models capable of handling complex causal inference tasks, including interventions and counterfactuals. These models not only maintain causal integrity but also offer practical solutions to real-world challenges like unfairness. Furthermore, the convergence of causal analysis with graph databases presents a promising direction for automating the extraction and integration of causal models, facilitating data-driven decision-making across various scientific domains. This synergy between causal inference and graph databases aims to redefine property graph data models and query semantics, paving the way for more sophisticated and personalized data applications.