mlsanityData to 14 Oct 2026

Causal Discovery Toolbox: Uncover causal relationships in Python

arXiv 1903.02278 ↗First posted 6 Mar 2019stat.CO stat.MLcited by 49

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Abstract (arXiv)

This paper presents a new open source Python framework for causal discovery from observational data and domain background knowledge, aimed at causal graph and causal mechanism modeling. The 'cdt' package implements the end-to-end approach, recovering the direct dependencies (the skeleton of the causal graph) and the causal relationships between variables. It includes algorithms from the 'Bnlearn' and 'Pcalg' packages, together with algorithms for pairwise causal discovery such as ANM. 'cdt' is available under the MIT License at https://github.com/Diviyan-Kalainathan/CausalDiscoveryToolbox.

Key facts. Cited by 49 papers in the map (data through 14 Oct 2026). First posted on arXiv 6 Mar 2019.

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Citing papers per year: 2021: 9; 2022: 17; 2023: 10; 2024: 7; 2025: 3; 2026: 3.

49 papers.

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  1. 2021D'ya like DAGs? A Survey on Structure Learning and Causal Discoverycited by 211
  2. 2021DiBS: Differentiable Bayesian Structure Learningcited by 106
  3. 2023A Survey on Causal Discovery: Theory and Practicecited by 63
  4. 2022Large-Scale Differentiable Causal Discovery of Factor Graphscited by 47
  5. 2022DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal modelscited by 41
  6. 2021Learning Neural Causal Models with Active Interventionscited by 40
  7. 2022Active Bayesian Causal Inferencecited by 35
  8. 2023A Survey on Causal Discovery Methods for I.I.D. and Time Series Datacited by 35
  9. 2023CRAB: Assessing the Strength of Causal Relationships Between Real-world Eventscited by 28
  10. 2022BaCaDI: Bayesian Causal Discovery with Unknown Interventionscited by 26
  11. 2022Evaluation Methods and Measures for Causal Learning Algorithmscited by 23
  12. 2022Truncated Matrix Power Iteration for Differentiable DAG Learningcited by 23
37 more
  1. 2024ChatDiet: Empowering Personalized Nutrition-Oriented Food Recommender Chatbots through an LLM-Augmented Frameworkcited by 20
  2. 2022D-BIAS: A Causality-Based Human-in-the-Loop System for Tackling Algorithmic Biascited by 18
  3. 2022Causal Discovery for Fairnesscited by 16
  4. 2021Model-Based Counterfactual Synthesizer for Interpretationcited by 14
  5. 2021ML4C: Seeing Causality Through Latent Vicinitycited by 11
  6. 2022A Meta-Reinforcement Learning Algorithm for Causal Discoverycited by 11
  7. 2022Towards Privacy-Aware Causal Structure Learning in Federated Settingcited by 11
  8. 2022CIPCaD-Bench: Continuous Industrial Process datasets for benchmarking Causal Discovery methodscited by 10
  9. 2023DAG Learning on the Permutahedroncited by 9
  10. 2021BeFair: Addressing Fairness in the Banking Sectorcited by 8
  11. 2022Unifying Pairwise Interactions in Complex Dynamicscited by 8
  12. 2023Learning DAGs from Data with Few Root Causescited by 8
  13. 2022Granger Causal Chain Discovery for Sepsis-Associated Derangements via Continuous-Time Hawkes Processescited by 6
  14. 2021Marginalizable Density Modelscited by 4
  15. 2023Learning Causal Graphs via Monotone Triangular Transport Mapscited by 4
  16. 2024CausalPlayground: Addressing Data-Generation Requirements in Cutting-Edge Causality Researchcited by 4
  17. 2025The Robustness of Differentiable Causal Discovery in Misspecified Scenarioscited by 4
  18. 2021A Fast PC Algorithm with Reversed-order Pruning and A Parallelization Strategycited by 3
  19. 2022Do learned representations respect causal relationships?cited by 3
  20. 2023Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimationcited by 3
  21. 2024Causal Reasoning in Software Quality Assurance: A Systematic Reviewcited by 3
  22. 2022Reframed GES with a Neural Conditional Dependence Measurecited by 2
  23. 2023Salesforce CausalAI Library: A Fast and Scalable Framework for Causal Analysis of Time Series and Tabular Datacited by 2
  24. 2024Causality for Earth Science -- A Review on Time-series and Spatiotemporal Causality Methodscited by 2
  25. 2024OCDB: Revisiting Causal Discovery with a Comprehensive Benchmark and Evaluation Frameworkcited by 2
  26. 2021Identifying Causal Influences on Publication Trends and Behavior: A Case Study of the Computational Linguistics Communitycited by 1
  27. 2022BCDAG: An R package for Bayesian structure and Causal learning of Gaussian DAGscited by 1
  28. 2022Causal Explanation for Reinforcement Learning: Quantifying State and Temporal Importancecited by 1
  29. 2024Generalized Criterion for Identifiability of Additive Noise Models Using Majorizationcited by 1
  30. 2025Differentially Private and Federated Structure Learning in Bayesian Networkscited by 1
  31. 2026Bivariate Causal Discovery Using Rate-Distortion MDL: An Information Dimension Approachcited by 1
  32. 2026Consistency evaluation of benchmarks used for causal discoverycited by 1
  33. 2023UPREVE: An End-to-End Causal Discovery Benchmarking Systemcited by 0
  34. 2023Can We Utilize Pre-trained Language Models within Causal Discovery Algorithms?cited by 0
  35. 2024Causal Discovery by Kernel Deviance Measures with Heterogeneous Transformscited by 0
  36. 2025LxCIM: a new rank-based binary classifier performance metric invariant to local exchange of classescited by 0
  37. 2026Fed-ReMasker: Federated Tabular Imputation under Feature-Level Missingnesscited by 0