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

arXiv: https://arxiv.org/abs/2301.08490 · HTML: https://mlsanity.com/paper/2301.08490/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 19 references that are in the map.
- First posted on arXiv 20 Jan 2023.

## Builds on (most cited first, 2 of 2)

- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [CausalKG: Causal Knowledge Graph Explainability using interventional and counterfactual reasoning](https://mlsanity.com/paper/2201.03647/) (2022)

## Built on by (most cited first, 1 of 1)

- [Beyond LLMs: A Linguistic Approach to Causal Graph Generation from Narrative Texts](https://mlsanity.com/paper/2504.07459/) (2025)

## Abstract (arXiv)

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.