mlsanityData to 7 Oct 2026

DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models

arXiv 2206.06821 ↗First posted 14 Jun 2022stat.ME cs.AI stat.MLcited by 40

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Abstract (arXiv)

We present DoWhy-GCM, an extension of the DoWhy Python library, which leverages graphical causal models. Unlike existing causality libraries, which mainly focus on effect estimation, DoWhy-GCM addresses diverse causal queries, such as identifying the root causes of outliers and distributional changes, attributing causal influences to the data generating process of each node, or diagnosis of causal structures. With DoWhy-GCM, users typically specify cause-effect relations via a causal graph, fit causal mechanisms, and pose causal queries -- all with just a few lines of code. The general documentation is available at https://www.pywhy.org/dowhy and the DoWhy-GCM specific code at https://github.com/py-why/dowhy/tree/main/dowhy/gcm.

Key facts. Cited by 40 papers in the map (data through 7 Oct 2026). Builds on 9 of its 29 references that are in the map. First posted on arXiv 14 Jun 2022.

Builds on

9 of 29 references.

References · 9

  1. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  2. 2019Explaining Machine Learning Classifiers through Diverse Counterfactual Explanationscited by 612
  3. 2019Feature relevance quantification in explainable AI: A causal problemcited by 164
  4. 2020DoWhy: An End-to-End Library for Causal Inferencecited by 92
  5. 2020CausalML: Python Package for Causal Machine Learningcited by 74
  6. 2019Causal structure based root cause analysis of outlierscited by 65
  7. 2019Causal Discovery Toolbox: Uncover causal relationships in Pythoncited by 49
  8. 2021Why did the distribution change?cited by 37
  9. 2020Quantifying intrinsic causal contributions via structure preserving interventionscited by 12

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 10; 2024: 8; 2025: 11; 2026: 11.

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  1. 2025Do-PFN: In-Context Learning for Causal Effect Estimationcited by 46
  2. 2023Modeling Causal Mechanisms with Diffusion Models for Interventional and Counterfactual Queriescited by 23
  3. 2023pgmpy: A Python Toolkit for Bayesian Networkscited by 21
  4. 2023Toward Falsifying Causal Graphs Using a Permutation-Based Testcited by 11
  5. 2024Cloud Atlas: Efficient Fault Localization for Cloud Systems using Language Models and Causal Insightcited by 11
  6. 2025DeCaFlow: A deconfounding causal generative modelcited by 8
  7. 2024A Fixed-Point Approach for Causal Generative Modelingcited by 7
  8. 2024Causality for Tabular Data Synthesis: A High-Order Structure Causal Benchmark Frameworkcited by 7
  9. 2023Dataflow graphs as complete causal graphscited by 5
  10. 2024Amortized Inference of Causal Models via Conditional Fixed-Point Iterationscited by 4
  11. 2025Root Cause Analysis of Outliers in Unknown Cyclic Graphscited by 4
  12. 2025Improving the Generation and Evaluation of Synthetic Data for Downstream Medical Causal Inferencecited by 4
28 more
  1. 2023Reinterpreting causal discovery as the task of predicting unobserved joint statisticscited by 3
  2. 2023Root Cause Explanation of Outliers under Noisy Mechanismscited by 3
  3. 2024Multiply-Robust Causal Change Attributioncited by 3
  4. 2024Causal-discovery-based root-cause analysis and its application in time-series prediction error diagnosiscited by 3
  5. 2025ProRCA: A Causal Python Package for Actionable Root Cause Analysis in Real-world Business Scenarioscited by 3
  6. 2026FoundCause: Causal Discovery with Latent Confounders from Observational Datacited by 3
  7. 2023Causal Dependence Plotscited by 2
  8. 2023Beyond Single-Feature Importance with ICECREAMcited by 2
  9. 2024Causality for Earth Science -- A Review on Time-series and Spatiotemporal Causality Methodscited by 2
  10. 2026A Causal Foundation Model for Structure and Outcome Predictioncited by 2
  11. 2023Reasoning-Based Software Testingcited by 1
  12. 2023Causal fault localisation in dataflow systemscited by 1
  13. 2024Towards Automated Causal Discovery: a case study on 5G telecommunication datacited by 1
  14. 2025ORCA: ORchestrating Causal Agentcited by 1
  15. 2025A Computational Theory for Efficient Mini Agent Evaluation with Causal Guaranteescited by 0
  16. 2025On Measuring Intrinsic Causal Attributions in Deep Neural Networkscited by 0
  17. 2025Evaluating and Learning Robust Bandit Policies Under Uncertain Causal Mechanismscited by 0
  18. 2025Root Cause Analysis of Hydrogen Bond Separation in Spatio-Temporal Molecular Dynamics using Causal Modelscited by 0
  19. 2025Causal Reinforcement Learning based Agent-Patient Interaction with Clinical Domain Knowledgecited by 0
  20. 2026Score-based Integrated Gradient for Root Cause Explanations of Outlierscited by 0
  21. 2026Uncertainty-aware Causal Decision Making via Effect Bound Decompositioncited by 0
  22. 2026Causal explanations of outliers in systems with lagged time-dependenciescited by 0
  23. 2026Effect-Level Validation for Causal Discoverycited by 0
  24. 2026Kolmogorov-Arnold causal generative modelscited by 0
  25. 2026Causal Parametric Drift Simulation: A Digital Twin Framework for Classifier Robustness Evaluationcited by 0
  26. 2026When Emotion Becomes Trigger: Emotion-style dynamic Backdoor Attack Parasitising Large Language Modelscited by 0
  27. 2026A Step Towards Inherently Interpretable Causal Machine Learning Models For Decision Supportcited by 0
  28. 2026Falsifying Causal Graphs With Outlier Eventscited by 0