mlsanityData to 7 Oct 2026

Causal-learn: Causal Discovery in Python

arXiv 2307.16405 ↗First posted 31 Jul 2023cs.LG stat.ME stat.MLcited by 108

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Abstract (arXiv)

Causal discovery aims at revealing causal relations from observational data, which is a fundamental task in science and engineering. We describe textitcausal-learn, an open-source Python library for causal discovery. This library focuses on bringing a comprehensive collection of causal discovery methods to both practitioners and researchers. It provides easy-to-use APIs for non-specialists, modular building blocks for developers, detailed documentation for learners, and comprehensive methods for all. Different from previous packages in R or Java, textitcausal-learn is fully developed in Python, which could be more in tune with the recent preference shift in programming languages within related communities. The library is available at https://github.com/py-why/causal-learn.

Key facts. Cited by 108 papers in the map (data through 7 Oct 2026). Builds on 8 of its 40 references that are in the map. First posted on arXiv 31 Jul 2023.

Builds on

8 of 40 references.

References · 8

  1. 2019Causal Discovery from Heterogeneous/Nonstationary Data with Independent Changescited by 166
  2. 2013Causal Inference in the Presence of Latent Variables and Selection Biascited by 165
  3. 2020DoWhy: An End-to-End Library for Causal Inferencecited by 92
  4. 2022Greedy Relaxations of the Sparsest Permutation Algorithmcited by 63
  5. 2021gCastle: A Python Toolbox for Causal Discoverycited by 60
  6. 2018Causal Discovery in the Presence of Missing Datacited by 46
  7. 2013Theory Refinement on Bayesian Networkscited by 28
  8. 2023Ananke: A Python Package For Causal Inference Using Graphical Modelscited by 8

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 6; 2024: 30; 2025: 39; 2026: 33.

108 papers.

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  1. 2024Efficient Causal Graph Discovery Using Large Language Modelscited by 51
  2. 2023A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variablescited by 33
  3. 2024Integrating Large Language Models in Causal Discovery: A Statistical Causal Approachcited by 31
  4. 2024Causal Inference with Large Language Model: A Surveycited by 31
  5. 2024Sample, estimate, aggregate: A recipe for causal discovery foundation modelscited by 19
  6. 2025Causally Reliable Concept Bottleneck Modelscited by 17
  7. 2025Prediction-Powered E-Valuescited by 16
  8. 2023Structured Neural Networks for Density Estimation and Causal Inferencecited by 13
  9. 2024ADAM: An Embodied Causal Agent in Open-World Environmentscited by 13
  10. 2025Multi-Scale Finetuning for Encoder-based Time Series Foundation Modelscited by 13
  11. 2024Gene Regulatory Network Inference in the Presence of Dropouts: a Causal Viewcited by 11
  12. 2024Local Causal Structure Learning in the Presence of Latent Variablescited by 11
38 more
  1. 2024Exploring Multi-Modal Data with Tool-Augmented LLM Agents for Precise Causal Discoverycited by 11
  2. 2024Causal Agent based on Large Language Modelcited by 10
  3. 2023LLM4Causal: Democratized Causal Tools for Everyone via Large Language Modelcited by 9
  4. 2024Detecting and Identifying Selection Structure in Sequential Datacited by 9
  5. 2025When Selection Meets Intervention: Additional Complexities in Causal Discoverycited by 9
  6. 2024Causality for Tabular Data Synthesis: A High-Order Structure Causal Benchmark Frameworkcited by 7
  7. 2024Effective Bayesian Causal Inference via Structural Marginalisation and Autoregressive Orderscited by 6
  8. 2025Learning General Causal Structures with Hidden Dynamic Process for Climate Analysiscited by 6
  9. 2024Hybrid Top-Down Global Causal Discovery with Local Search for Linear and Nonlinear Additive Noise Modelscited by 5
  10. 2024Deriving Causal Order from Single-Variable Interventions: Guarantees & Algorithmcited by 5
  11. 2023When Graph Neural Network Meets Causality: Opportunities, Methodologies and An Outlookcited by 4
  12. 2024Tangent Space Causal Inference: Leveraging Vector Fields for Causal Discovery in Dynamical Systemscited by 4
  13. 2025Can LLMs Leverage Observational Data? Towards Data-Driven Causal Discovery with LLMscited by 4
  14. 2025The Third Pillar of Causal Analysis? A Measurement Perspective on Causal Representationscited by 4
  15. 2025The Robustness of Differentiable Causal Discovery in Misspecified Scenarioscited by 4
  16. 2025Differentiable Constraint-Based Causal Discoverycited by 4
  17. 2026Zero-Flow Encoderscited by 4
  18. 2026Latent Confounded Causal Discovery via Lie Bracket Geometrycited by 4
  19. 2023Causal Discovery in Mixtures of Populationscited by 3
  20. 2024Causal Reasoning in Software Quality Assurance: A Systematic Reviewcited by 3
  21. 2025Falsification of Unconfoundedness by Testing Independence of Causal Mechanismscited by 3
  22. 2025SNAP: Sequential Non-Ancestor Pruning for Targeted Causal Effect Estimation With an Unknown Graphcited by 3
  23. 2025On Different Notions of Redundancy in Conditional-Independence-Based Discovery of Graphical Modelscited by 3
  24. 2025Learning Identifiable Structures Helps Avoid Bias in DNN-based Supervised Causal Learningcited by 3
  25. 2025Causal Discovery and Counterfactual Reasoning to Optimize Persuasive Dialogue Policiescited by 3
  26. 2025Causal Discovery via Quantile Partial Effectcited by 3
  27. 2025Revealing Multimodal Causality with Large Language Modelscited by 3
  28. 2025Nonparametric Identification of Latent Conceptscited by 3
  29. 2026Actively Obtaining Environmental Feedback for Autonomous Action Evaluation Without Predefined Measurementscited by 3
  30. 2024Causality for Earth Science -- A Review on Time-series and Spatiotemporal Causality Methodscited by 2
  31. 2024Fast-PGM: Fast Probabilistic Graphical Model Learning and Inferencecited by 2
  32. 2025A Fast Kernel-based Conditional Independence test with Application to Causal Discoverycited by 2
  33. 2025Counterfactually Fair Conformal Predictioncited by 2
  34. 2025Local Causal Discovery for Statistically Efficient Causal Inferencecited by 2
  35. 2025Understanding Hardness of Vision-Language Compositionality from A Token-level Causal Lenscited by 2
  36. 2025Foundation Models in Biomedical Imaging: Turning Hype into Realitycited by 2
  37. 2026Dynamic Expert-Guided Model Averaging for Causal Discoverycited by 2
  38. 2026Causal Discovery for Explainable AI: A Dual-Encoding Approachcited by 2

50 most cited of 108 All 108 (JSON)