mlsanityData to 7 Oct 2026

Causality Learning: A New Perspective for Interpretable Machine Learning

arXiv 2006.16789 ↗First posted 27 Jun 2020cs.LG cs.AI stat.MLcited by 28

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Abstract (arXiv)

Recent years have witnessed the rapid growth of machine learning in a wide range of fields such as image recognition, text classification, credit scoring prediction, recommendation system, etc. In spite of their great performance in different sectors, researchers still concern about the mechanism under any machine learning (ML) techniques that are inherently black-box and becoming more complex to achieve higher accuracy. Therefore, interpreting machine learning model is currently a mainstream topic in the research community. However, the traditional interpretable machine learning focuses on the association instead of the causality. This paper provides an overview of causal analysis with the fundamental background and key concepts, and then summarizes most recent causal approaches for interpretable machine learning. The evaluation techniques for assessing method quality, and open problems in causal interpretability are also discussed in this paper.

Key facts. Cited by 28 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Jun 2020.

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Citing papers per year: 2021: 8; 2022: 6; 2023: 4; 2024: 5; 2025: 4; 2026: 1.

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  1. 2021D'ya like DAGs? A Survey on Structure Learning and Causal Discoverycited by 209
  2. 2021Interpretable Deep Learning: Interpretation, Interpretability, Trustworthiness, and Beyondcited by 116
  3. 2022Leveraging Explanations in Interactive Machine Learning: An Overviewcited by 52
  4. 2024Mitigating Modality Prior-Induced Hallucinations in Multimodal Large Language Models via Deciphering Attention Causalitycited by 42
  5. 2023Causal Deep Learningcited by 20
  6. 2021Prescriptive Process Monitoring Under Resource Constraints: A Causal Inference Approachcited by 12
  7. 2023Interpretability of Machine Learning: Recent Advances and Future Prospectscited by 12
  8. 2022When to intervene? Prescriptive Process Monitoring Under Uncertainty and Resource Constraintscited by 11
  9. 2021Be Causal: De-biasing Social Network Confounding in Recommendationcited by 9
  10. 2024Towards Explainable Artificial Intelligence (XAI): A Data Mining Perspectivecited by 9
  11. 2023CeFlow: A Robust and Efficient Counterfactual Explanation Framework for Tabular Data using Normalizing Flowscited by 8
  12. 2021Causal Learning for Socially Responsible AIcited by 7
16 more
  1. 2021Deep Treatment-Adaptive Network for Causal Inferencecited by 5
  2. 2023Causal Inference for Banking Finance and Insurance A Surveycited by 5
  3. 2024Explain the Black Box for the Sake of Science: the Scientific Method in the Era of Generative Artificial Intelligencecited by 5
  4. 2025JAM: Controllable and Responsible Text Generation via Causal Reasoning and Latent Vector Manipulationcited by 5
  5. 2021Reinforcement Learning based Path Exploration for Sequential Explainable Recommendationcited by 4
  6. 2022A Review and Roadmap of Deep Learning Causal Discovery in Different Variable Paradigmscited by 4
  7. 2024CausalDiff: Causality-Inspired Disentanglement via Diffusion Model for Adversarial Defensecited by 4
  8. 2025Incremental Causal Graph Learning for Online Cyberattack Detection in Cyber-Physical Infrastructurescited by 4
  9. 2022On the Sparse DAG Structure Learning Based on Adaptive Lassocited by 3
  10. 2026The Trap of Trajectory: Towards Understanding and Mitigating Spurious Correlations in Agentic Memorycited by 3
  11. 2021Stochastic Intervention for Causal Effect Estimationcited by 2
  12. 2022Navigating causal deep learningcited by 1
  13. 2025REX: Causal discovery based on machine learning and explainability techniquescited by 1
  14. 2022Application of Causal Inference to Analytical Customer Relationship Management in Banking and Insurancecited by 0
  15. 2024OccludeNet: A Causal Journey into Mixed-View Actor-Centric Video Action Recognition under Occlusionscited by 0
  16. 2025Causal Machine Learning in IoT-based Engineering Problems: A Tool Comparison in the Case of Household Energy Consumptioncited by 0