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This paper describes a novel Python package, named causalgraph, for modelingand saving causal graphs embedded in knowledge graphs. The package has beendesigned to provide an interface between causal disciplines such as causaldiscovery and causal inference. With this package, users can create and savecausal graphs and export the generated graphs for use in other graph-basedpackages. The main advantage of the proposed package is its ability tofacilitate the linking of additional information and metadata to causalstructures. In addition, the package offers a variety of functions for graphmodeling and plotting, such as editing, adding, and deleting nodes and edges.It is also compatible with widely used graph data science libraries such asNetworkX and Tigramite and incorporates a specially developed causalgraphontology in the background. This paper provides an overview of the package'smain features, functionality, and usage examples, enabling the reader to usethe package effectively in practice.

causalgraph: A Python Package for Modeling, Persisting and Visualizing Causal Graphs Embedded in Knowledge Graphs