mlsanityData to 14 Oct 2026

Counterfactual Fairness Evaluation of Machine Learning Models on Educational Datasets

arXiv 2504.11504 ↗First posted 15 Apr 2025cs.CY cs.LGcited by 3

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Abstract (arXiv)

As machine learning models are increasingly used in educational settings, from detecting at-risk students to predicting student performance, algorithmic bias and its potential impacts on students raise critical concerns about algorithmic fairness. Although group fairness is widely explored in education, works on individual fairness in a causal context are understudied, especially on counterfactual fairness. This paper explores the notion of counterfactual fairness for educational data by conducting counterfactual fairness analysis of machine learning models on benchmark educational datasets. We demonstrate that counterfactual fairness provides meaningful insight into the causality of sensitive attributes and causal-based individual fairness in education.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 24 of its 54 references that are in the map. First posted on arXiv 15 Apr 2025.

Builds on

24 of 54 references.

References · 24

  1. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
  2. 2016Equality of Opportunity in Supervised Learningcited by 2,007
  3. 2017Counterfactual Fairnesscited by 804
  4. 2015Deep Knowledge Tracingcited by 332
  5. 2021A survey on datasets for fairness-aware machine learningcited by 157
  6. 2018Counterfactual Fairness in Text Classification through Robustnesscited by 151
  7. 2019On the Apparent Conflict Between Individual and Group Fairnesscited by 126
  8. 2022Bias Mitigation for Machine Learning Classifiers: A Comprehensive Surveycited by 125
  9. 2020Evaluating and Mitigating Bias in Image Classifiers: A Causal Perspective Using Counterfactualscited by 56
  10. 2018Causal Reasoning for Algorithmic Fairnesscited by 51
  11. 2019Counterfactual Reasoning for Fair Clinical Risk Predictioncited by 28
  12. 2022Addressing Fairness Issues in Deep Learning-Based Medical Image Analysis: A Systematic Reviewcited by 28
12 more
  1. 2020Machine learning fairness notions: Bridging the gap with real-world applicationscited by 19
  2. 2020Equality of Learning Opportunity via Individual Fairness in Personalized Recommendationscited by 13
  3. 2021Towards Equity and Algorithmic Fairness in Student Grade Predictioncited by 13
  4. 2023Learning for Counterfactual Fairness from Observational Datacited by 13
  5. 2022A Survey of Deep Causal Models and Their Industrial Applicationscited by 10
  6. 2023Counterfactually Fair Representationcited by 9
  7. 2024Counterfactual Fairness by Combining Factual and Counterfactual Predictionscited by 8
  8. 2023Is Your Model "MADD"? A Novel Metric to Evaluate Algorithmic Fairness for Predictive Student Modelscited by 7
  9. 2022An intersectional framework for counterfactual fairness in risk predictioncited by 6
  10. 2024On the (In)Compatibility between Group Fairness and Individual Fairnesscited by 6
  11. 2022Protected Attributes Tell Us Who, Behavior Tells Us How: A Comparison of Demographic and Behavioral Oversampling for Fair Student Success Modelingcited by 3
  12. 2023Achieving Counterfactual Fairness with Imperfect Structural Causal Modelcited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Causal Pre-training Under the Fairness Lens: An Empirical Study of TabPFNcited by 0
  2. 2026Fairness under Graph Uncertainty: Achieving Interventional Fairness with Partially Known Causal Graphs over Clusters of Variablescited by 0
  3. 2026Side-by-side Comparison Amplifies Dialect Bias in Language Modelscited by 0