The CausalPlayground library serves as a tool for causality research, focusing on the interactive exploration of structural causal models (SCMs). It provides extensive functionality for creating, manipulating and sampling SCMs, seamlessly integrating them with the Gymnasium framework. Users have complete control over SCMs, enabling precise manipulation and interaction with causal mechanisms. Additionally, CausalPlayground offers a range of useful helper functions for generating diverse instances of SCMs and DAGs, facilitating quantitative experimentation and evaluation. Notably, the library is optimized for (but not limited to) easy integration with reinforcement learning methods, enhancing its utility in active inference and learning settings. Find the complete API documentation and a quickstart guide here.

In your python environment pip install causal-playground.

Contributions are highly welcomed and encouraged! To contribute to the project, please follow the following steps:

• Fork the project.
• Create a local branch my-awesome-new-feature.
• Implement your new feature in the newly created branch.
• Make sure you provide sufficient documentation and test-cases.
• Open a pull request.

Alternatively, you can open a well-described issue.

If you are using this library, please consider citing our paper:

@misc{sauter2024causalplayground,
      title={CausalPlayground: Addressing Data-Generation Requirements in Cutting-Edge Causality Research}, 
      author={Andreas W M Sauter and Erman Acar and Aske Plaat},
      year={2024},
      eprint={2405.13092},
      archivePrefix={arXiv},
      primaryClass={cs.AI}
}