# Counterfactual Fairness Evaluation of Machine Learning Models on Educational Datasets

arXiv: https://arxiv.org/abs/2504.11504 · HTML: https://mlsanity.com/paper/2504.11504/

## 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 (most cited first, 24 of 24)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [Counterfactual Fairness](https://mlsanity.com/paper/1703.06856/) (2017)
- [Deep Knowledge Tracing](https://mlsanity.com/paper/1506.05908/) (2015)
- [A survey on datasets for fairness-aware machine learning](https://mlsanity.com/paper/2110.00530/) (2021)
- [Counterfactual Fairness in Text Classification through Robustness](https://mlsanity.com/paper/1809.10610/) (2018)
- [On the Apparent Conflict Between Individual and Group Fairness](https://mlsanity.com/paper/1912.06883/) (2019)
- [Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey](https://mlsanity.com/paper/2207.07068/) (2022)
- [Evaluating and Mitigating Bias in Image Classifiers: A Causal Perspective Using Counterfactuals](https://mlsanity.com/paper/2009.08270/) (2020)
- [Causal Reasoning for Algorithmic Fairness](https://mlsanity.com/paper/1805.05859/) (2018)
- [Counterfactual Reasoning for Fair Clinical Risk Prediction](https://mlsanity.com/paper/1907.06260/) (2019)
- [Addressing Fairness Issues in Deep Learning-Based Medical Image Analysis: A Systematic Review](https://mlsanity.com/paper/2209.13177/) (2022)
- [Machine learning fairness notions: Bridging the gap with real-world applications](https://mlsanity.com/paper/2006.16745/) (2020)
- [Equality of Learning Opportunity via Individual Fairness in Personalized Recommendations](https://mlsanity.com/paper/2006.04282/) (2020)
- [Towards Equity and Algorithmic Fairness in Student Grade Prediction](https://mlsanity.com/paper/2105.06604/) (2021)
- [Learning for Counterfactual Fairness from Observational Data](https://mlsanity.com/paper/2307.08232/) (2023)
- [A Survey of Deep Causal Models and Their Industrial Applications](https://mlsanity.com/paper/2209.08860/) (2022)
- [Counterfactually Fair Representation](https://mlsanity.com/paper/2311.05420/) (2023)
- [Counterfactual Fairness by Combining Factual and Counterfactual Predictions](https://mlsanity.com/paper/2409.01977/) (2024)
- [Is Your Model "MADD"? A Novel Metric to Evaluate Algorithmic Fairness for Predictive Student Models](https://mlsanity.com/paper/2305.15342/) (2023)
- [An intersectional framework for counterfactual fairness in risk prediction](https://mlsanity.com/paper/2210.01194/) (2022)
- [On the (In)Compatibility between Group Fairness and Individual Fairness](https://mlsanity.com/paper/2401.07174/) (2024)
- [Protected Attributes Tell Us Who, Behavior Tells Us How: A Comparison of Demographic and Behavioral Oversampling for Fair Student Success Modeling](https://mlsanity.com/paper/2212.10166/) (2022)
- [Achieving Counterfactual Fairness with Imperfect Structural Causal Model](https://mlsanity.com/paper/2303.14665/) (2023)

## Built on by (most cited first, 3 of 3)

- [Causal Pre-training Under the Fairness Lens: An Empirical Study of TabPFN](https://mlsanity.com/paper/2601.17912/) (2026)
- [Fairness under Graph Uncertainty: Achieving Interventional Fairness with Partially Known Causal Graphs over Clusters of Variables](https://mlsanity.com/paper/2602.23611/) (2026)
- [Side-by-side Comparison Amplifies Dialect Bias in Language Models](https://mlsanity.com/paper/2605.24384/) (2026)

## 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.