gCastle: A Python Toolbox for Causal Discovery
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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, includes many recently developed gradient-based causal discovery methods with optional GPU acceleration. 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. is available under Apache License 2.0 at <https://github.com/huawei-noah/trustworthyAI/tree/master/gcastle>.
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