gCastle: A Python Toolbox for Causal Discovery
This is an incremental contribution providing a convenient tool for researchers and practitioners in causal discovery, particularly in telecommunications.
The authors introduced gCastle, a Python toolbox for causal discovery that integrates data generation, structure learning, and evaluation, including gradient-based methods with GPU acceleration, and demonstrated its utility with three real-world telecommunications datasets.
$\texttt{gCastle}$ is an end-to-end Python toolbox for causal structure learning. It provides functionalities of generating data from either simulator or real-world dataset, learning causal structure from the data, and evaluating the learned graph, together with useful practices such as prior knowledge insertion, preliminary neighborhood selection, and post-processing to remove false discoveries. Compared with related packages, $\texttt{gCastle}$ includes many recently developed gradient-based causal discovery methods with optional GPU acceleration. $\texttt{gCastle}$ brings convenience to researchers who may directly experiment with the code as well as practitioners with graphical user interference. Three real-world datasets in telecommunications are also provided in the current version. $\texttt{gCastle}$ is available under Apache License 2.0 at \url{https://github.com/huawei-noah/trustworthyAI/tree/master/gcastle}.