CausalPlayground: Addressing Data-Generation Requirements in Cutting-Edge Causality Research
<|reference_start|>CausalPlayground: Addressing Data-Generation Requirements in Cutting-Edge Causality Research: Research on causal effects often relies on synthetic data due to the scarcity of real-world datasets with ground-truth effects. Since current data-generating tools do not always meet all requirements for state-of-the-art research, ad-hoc methods are often employed. This leads to heterogeneity among datasets and delays research progress. We address the shortcomings of current data-generating libraries by introducing CausalPlayground, a Python library that provides a standardized platform for generating, sampling, and sharing structural causal models (SCMs). CausalPlayground offers fine-grained control over SCMs, interventions, and the generation of datasets of SCMs for learning and quantitative research. Furthermore, by integrating with Gymnasium, the standard framework for reinforcement learning (RL) environments, we enable online interaction with the SCMs. Overall, by introducing CausalPlayground we aim to foster more efficient and comparable research in the field. All code and API documentation is available at https://github.com/sa-and/CausalPlayground.<|reference_end|>
@article{sauter2024causalplayground:,
    title={CausalPlayground: Addressing Data-Generation Requirements in
  Cutting-Edge Causality Research},
    author={Andreas W M Sauter, Erman Acar, Aske Plaat},
    journal={arXiv preprint arXiv:2405.13092},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.13092},
    primaryClass={cs.AI cs.LG}
}