mlsanityData to 7 Oct 2026

Causal Feature Selection for Responsible Machine Learning

arXiv 2402.02696 ↗First posted 5 Feb 2024cs.LG cs.AIcited by 2

Not on the map. Why?

Abstract (arXiv)

Machine Learning (ML) has become an integral aspect of many real-world applications. As a result, the need for responsible machine learning has emerged, focusing on aligning ML models to ethical and social values, while enhancing their reliability and trustworthiness. Responsible ML involves many issues. This survey addresses four main issues: interpretability, fairness, adversarial robustness, and domain generalization. Feature selection plays a pivotal role in the responsible ML tasks. However, building upon statistical correlations between variables can lead to spurious patterns with biases and compromised performance. This survey focuses on the current study of causal feature selection: what it is and how it can reinforce the four aspects of responsible ML. By identifying features with causal impacts on outcomes and distinguishing causality from correlation, causal feature selection is posited as a unique approach to ensuring ML models to be ethically and socially responsible in high-stakes applications.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 39 of its 61 references that are in the map. First posted on arXiv 5 Feb 2024.

Builds on

39 of 61 references.

References · 39

  1. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  2. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  3. 2013Intriguing properties of neural networkscited by 5,114
  4. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  5. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
  6. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,543
  7. 2017Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPRcited by 1,086
  8. 2019Adversarial Examples Are Not Bugs, They Are Featurescited by 950
  9. 2017Counterfactual Fairnesscited by 802
  10. 2019Explaining Machine Learning Classifiers through Diverse Counterfactual Explanationscited by 612
  11. 2015Causal inference using invariant prediction: identification and confidence intervalscited by 533
  12. 2021Causal Abstractions of Neural Networkscited by 401
27 more
  1. 2020Algorithmic Recourse: from Counterfactual Explanations to Interventionscited by 250
  2. 2023Causal Abstraction: A Theoretical Foundation for Mechanistic Interpretabilitycited by 194
  3. 2017Avoiding Discrimination through Causal Reasoningcited by 191
  4. 2019Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifierscited by 143
  5. 2021Salient ImageNet: How to discover spurious features in Deep Learning?cited by 114
  6. 2018Abstracting Causal Modelscited by 104
  7. 2019CXPlain: Causal Explanations for Model Interpretation under Uncertaintycited by 98
  8. 2019Neural Network Attributions: A Causal Perspectivecited by 77
  9. 2020Opportunities and Challenges in Deep Learning Adversarial Robustness: A Surveycited by 71
  10. 2020Towards Unifying Feature Attribution and Counterfactual Explanations: Different Means to the Same Endcited by 66
  11. 2020Survey on Causal-based Machine Learning Fairness Notionscited by 53
  12. 2017Causally Regularized Learning with Agnostic Data Selection Biascited by 29
  13. 2020Causal Feature Selection for Algorithmic Fairnesscited by 25
  14. 2020Machine Learning for Reliability Engineering and Safety Applications: Review of Current Status and Future Opportunitiescited by 22
  15. 2022Counterfactual Fairness with Partially Known Causal Graphcited by 20
  16. 2019Explaining Visual Models by Causal Attributioncited by 18
  17. 2022Domain Generalization -- A Causal Perspectivecited by 14
  18. 2018Granger-causal Attentive Mixtures of Experts: Learning Important Features with Neural Networkscited by 13
  19. 2022Causality in Configurable Software Systemscited by 13
  20. 2020Towards a More Reliable Interpretation of Machine Learning Outputs for Safety-Critical Systems using Feature Importance Fusioncited by 11
  21. 2019Attribution-driven Causal Analysis for Detection of Adversarial Examplescited by 8
  22. 2021Instance-wise Causal Feature Selection for Model Interpretationcited by 8
  23. 2021Selecting Treatment Effects Models for Domain Adaptation Using Causal Knowledgecited by 4
  24. 2023Selecting Robust Features for Machine Learning Applications using Multidata Causal Discoverycited by 4
  25. 2021Scalable Causal Domain Adaptationcited by 3
  26. 2023Fair Causal Feature Selectioncited by 2
  27. 2023Towards a responsible machine learning approach to identify forced labor in fisheriescited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2026Causally-Guided Automated Feature Engineering with Multi-Agent Reinforcement Learningcited by 3
  2. 2025Comprehend, Divide, and Conquer: Feature Subspace Exploration via Multi-Agent Hierarchical Reinforcement Learningcited by 1