Package: causalgraph
causalgraphA python package for modeling, persisting and visualizing causal graphs embedded in knowledge graphs.GitHub Repository·Contact usTable of contentsAbout the ProjectGetting startedUsageContributingLicenseContactSimilar ProjectsAcknowledgmentsCiting this projectAbout the projectcausalgraph - 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 asNetworkXandTigramiteand incorporates a specially developedcausalgraph ontologyin the background.Getting startedYou can either use the causalgraph source code directly, or install the python package using pip.Using pipJust install causalgraph via pip running the following command:pipinstallcausalgraphLocal source codeClone the repogitclonehttps://github.com/causalgraph/causalgraph.gitCreate conda environmentcondaenvcreate-fenvironment.yml
condaactivatecausalgraphReady to use causalgraphimportcausalgraphUsagePlease have a look at theexampleswithin the GitHub Repo . Here you can find some Jupyter notebooks in which the most important functions are presented. You can also take a look at the causalgraph paper. It is referenced in the end of this README.ContributingContributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make aregreatly appreciated.If you have a suggestion that would make this better, please fork theGitHub repoand create a pull request. You can also simply open an issue with the tag "enhancement".
Don't forget to give the project a star! Thanks again!Fork the ProjectCreate your Feature Branch (git checkout -b feature/AmazingFeature)Commit your Changes (git commit -m 'Add some AmazingFeature')Push to the Branch (git push origin feature/AmazingFeature)Open a Pull RequestLicenseDistributed under the MIT License.ContactMail:causalgraph@iwu.fraunhofer.deBlog:https://www.kognitive-produktion.de/?p=3154Project Link:https://github.com/causalgraphSimilar projectsowlready2- Makes Ontologies and Knowledge Graphs workable in python. Is used for storing informationnetworkX- Represent graphs in python; is used as inspiration for the Calls and structure of the projectCausal Graphical Models Python Package- Major inspiration for this package, but we want to do these things differently:Represent Nodes and Edges as individual objects, establishing the connection to datasources / modelsusage ofnetworkX MultiDiGraphas base for the SCMs to support multiple connections (at different times) between nodesCausal Inference in Statistics- Python Code accompanying a book, which also implements SCMs (without Time-Series)AcknowledgmentsThe development of causalgraph is part of the research project KausaLAssist. It is funded by the German Federal Ministry of Education and Research (BMBF) within the "Future of Value Creation - Research on Production, Services and Work" program (funding number 02P20A150) and managed by the Project Management Agency Karlsruhe (PTKA). The authors are responsible for the content of this publication.Citing this projectIf you find causalgraph useful for your research, please cite us as follows:Sven Pieper, Carl Willy Mehling, Dominik Hirsch, Tobias Lüke, Steffen Ihlenfeldt. causalgraph: A Python Package for Modeling, Persisting and Visualizing Causal Graphs Embedded in Knowledge Graphs. 2023.https://arxiv.org/abs/2301.08490DOI:10.48550/arXiv.2301.08490Bibtex:@misc{doi={10.48550/ARXIV.2301.08490},url = {https://arxiv.org/abs/2301.08490},author = {Pieper, Sven and Mehling, Carl Willy and Hirsch, Dominik and Lüke, Tobias and Ihlenfeldt, Steffen},keywords = {Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences, E.1; E.2},title = {causalgraph: A Python Package for Modeling, Persisting and Visualizing Causal Graphs Embedded in Knowledge Graphs},publisher = {arXiv},year = {2023}}