This paper describes a novel Python package, named causalgraph, for modeling and saving causal graphs embedded in knowledge graphs. The package has been designed to provide an interface between causal disciplines such as causal discovery and causal inference. With this package, users can create and save causal graphs and export the generated graphs for use in other graph-based packages. The main advantage of the proposed package is its ability to facilitate the linking of additional information and metadata to causal structures. In addition, the package offers a variety of functions for graph modeling and plotting, such as editing, adding, and deleting nodes and edges. It is also compatible with widely used graph data science libraries such as NetworkX and Tigramite and incorporates a specially developed causalgraph ontology in the background. This paper provides an overview of the package's main features, functionality, and usage examples, enabling the reader to use the package effectively in practice.

翻译：本文描述了一个新的Python软件包,称为因果图,用于模拟和保存嵌入知识图中的因果图。该软件包的设计是为了在因果发现和因果推断等因果学科之间提供一个接口。有了这个软件包,用户可以创建和保存因果图,并导出生成的图表,供其他基于图的软件包使用。拟议软件包的主要优点是能够便利将额外信息和元数据与因果结构联系起来。此外,软件包提供了各种图建和绘图功能,如编辑、添加和删除节点和边缘。软件包也与广用图解科学库如网络X和Tigramite兼容,并在背景中包含一个专门开发的因果图解。本文概述了软件包的主要特征、功能和使用实例,使读者能够在实践中有效地使用软件包。