mlsanityData to 14 Oct 2026

gCastle: A Python Toolbox for Causal Discovery

arXiv 2111.15155 ↗First posted 30 Nov 2021cs.LG stat.MLcited by 61

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Abstract (arXiv)

textttgCastle 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, textttgCastle includes many recently developed gradient-based causal discovery methods with optional GPU acceleration. textttgCastle 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. textttgCastle is available under Apache License 2.0 at urlhttps://github.com/huawei-noah/trustworthyAI/tree/master/gcastle.

Key facts. Cited by 61 papers in the map (data through 14 Oct 2026). Builds on 19 of its 35 references that are in the map. First posted on arXiv 30 Nov 2021.

Builds on

19 of 35 references.

References · 19

  1. 2018DAGs with NO TEARS: Continuous Optimization for Structure Learningcited by 752
  2. 2019DAG-GNN: DAG Structure Learning with Graph Neural Networkscited by 331
  3. 2017Detecting causal associations in large nonlinear time series datasetscited by 244
  4. 2019Gradient-Based Neural DAG Learningcited by 227
  5. 2020Differentiable Causal Discovery from Interventional Datacited by 218
  6. 2019Learning Sparse Nonparametric DAGscited by 207
  7. 2020On the Role of Sparsity and DAG Constraints for Learning Linear DAGscited by 191
  8. 2013CAM: Causal additive models, high-dimensional order search and penalized regressioncited by 178
  9. 2013Causal Inference and Causal Explanation with Background Knowledgecited by 170
  10. 2019Causal Discovery with Reinforcement Learningcited by 145
  11. 2020DoWhy: An End-to-End Library for Causal Inferencecited by 95
  12. 2019Masked Gradient-Based Causal Structure Learningcited by 89
7 more
  1. 2020CausalML: Python Package for Causal Machine Learningcited by 74
  2. 2020Differentiable Causal Discovery Under Unmeasured Confoundingcited by 56
  3. 2019A Graph Autoencoder Approach to Causal Structure Learningcited by 55
  4. 2021Ordering-Based Causal Discovery with Reinforcement Learningcited by 55
  5. 2015A fast PC algorithm for high dimensional causal discovery with multi-core PCscited by 45
  6. 2020On Low Rank Directed Acyclic Graphs and Causal Structure Learningcited by 23
  7. 2021An Influence-based Approach for Root Cause Alarm Discovery in Telecom Networkscited by 10

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 1; 2022: 5; 2023: 12; 2024: 14; 2025: 17; 2026: 12.

61 papers.

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  1. 2023Causal-learn: Causal Discovery in Pythoncited by 109
  2. 2022Deep End-to-end Causal Inferencecited by 95
  3. 2023A Survey on Causal Discovery Methods for I.I.D. and Time Series Datacited by 35
  4. 2023A Scale-Invariant Sorting Criterion to Find a Causal Order in Additive Noise Modelscited by 28
  5. 2021FedDAG: Federated DAG Structure Learningcited by 17
  6. 2023A Review of the Role of Causality in Developing Trustworthy AI Systemscited by 14
  7. 2022CIPCaD-Bench: Continuous Industrial Process datasets for benchmarking Causal Discovery methodscited by 10
  8. 2023Evaluation of Induced Expert Knowledge in Causal Structure Learning by NOTEARScited by 8
  9. 2023Applying Large Language Models for Causal Structure Learning in Non Small Cell Lung Cancercited by 8
  10. 2024Comprehensive Review and Empirical Evaluation of Causal Discovery Algorithms for Numerical Datacited by 8
  11. 2025Unitless Unrestricted Markov-Consistent SCM Generation: Better Benchmark Datasets for Causal Discoverycited by 8
  12. 2026FML-bench: A Controlled Study of AI Research Agent Strategies from the Perspective of Search Dynamicscited by 8
38 more
  1. 2023Open problems in causal structure learning: A case study of COVID-19 in the UKcited by 7
  2. 2024A Fixed-Point Approach for Causal Generative Modelingcited by 7
  3. 2024Effective Bayesian Causal Inference via Structural Marginalisation and Autoregressive Orderscited by 6
  4. 2025Structure Causal Models and LLMs Integration in Medical Visual Question Answeringcited by 6
  5. 2023Optimizing Data-driven Causal Discovery Using Knowledge-guided Searchcited by 5
  6. 2022Tearing Apart NOTEARS: Controlling the Graph Prediction via Variance Manipulationcited by 4
  7. 2023Robustness of Algorithms for Causal Structure Learning to Hyperparameter Choicecited by 4
  8. 2024Permutation-based Inference for Variational Learning of Directed Acyclic Graphscited by 4
  9. 2024CausalPlayground: Addressing Data-Generation Requirements in Cutting-Edge Causality Researchcited by 4
  10. 2025Causal Discovery via Quantile Partial Effectcited by 4
  11. 2025The Robustness of Differentiable Causal Discovery in Misspecified Scenarioscited by 4
  12. 2022Learning Multiscale Non-stationary Causal Structurescited by 3
  13. 2023A Causal Ordering Prior for Unsupervised Representation Learningcited by 3
  14. 2023Learned Causal Method Predictioncited by 3
  15. 2024Reinforcement Learning for Causal Discovery without Acyclicity Constraintscited by 3
  16. 2025ACCESS : A Benchmark for Abstract Causal Event Discovery and Reasoningcited by 3
  17. 2025Learning Identifiable Structures Helps Avoid Bias in DNN-based Supervised Causal Learningcited by 3
  18. 2024Generalization of LiNGAM that allows confoundingcited by 2
  19. 2024Weakly-supervised causal discovery based on fuzzy knowledge and complex data complementaritycited by 2
  20. 2024OCDB: Revisiting Causal Discovery with a Comprehensive Benchmark and Evaluation Frameworkcited by 2
  21. 2025Causal Explainability of Machine Learning in Heart Failure Prediction from Electronic Health Recordscited by 2
  22. 2025Robust Causal Discovery under Imperfect Structural Constraintscited by 2
  23. 2025Cluster-Dags as Powerful Background Knowledge For Causal Discoverycited by 2
  24. 2022Causal Explanation for Reinforcement Learning: Quantifying State and Temporal Importancecited by 1
  25. 2024ExDAG: an MIQP Algorithm for Learning DAGscited by 1
  26. 2024Anwendung von Causal-Discovery-Algorithmen zur Root-Cause-Analyse in der Fahrzeugmontagecited by 1
  27. 2025Causal Discovery via Bayesian Optimizationcited by 1
  28. 2025Heterogeneous Causal Discovery of Repeated Undesirable Health Outcomescited by 1
  29. 2025CAMA: Enhancing Mathematical Reasoning in Large Language Models with Causal Knowledgecited by 1
  30. 2023UPREVE: An End-to-End Causal Discovery Benchmarking Systemcited by 0
  31. 2024Neural Networks with Causal Graph Constraints: A New Approach for Treatment Effects Estimationcited by 0
  32. 2024Inference of Causal Networks using a Topological Thresholdcited by 0
  33. 2024Scalable Temporal Anomaly Causality Discovery in Large Systems: Achieving Computational Efficiency with Binary Anomaly Flag Datacited by 0
  34. 2025A Guide to Bayesian Networks Software Packages for Structure and Parameter Learning -- 2025 Editioncited by 0
  35. 2025A Computational Theory for Efficient Mini Agent Evaluation with Causal Guaranteescited by 0
  36. 2025Causality-Induced Positional Encoding for Transformer-Based Representation Learning of Non-Sequential Featurescited by 0
  37. 2025Guide: Generalized-Prior and Data Encoders for DAG Estimationcited by 0
  38. 2025Competition is the key: A Game Theoretic Causal Discovery Approachcited by 0

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