Research map / Learning theory and algorithms / Fairness
Fairness without Imputation: A Decision Tree Approach for Fair Prediction with Missing Values
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use Fair MIP Forest instead of fair learning methods applied to imputed data | COMPAS · COMPAS | accuracy at a given FNR difference | Helped |
| Use Fair MIP Forest or Agarwal with mean imputation instead of other tested methods | Adult fair prediction · Adult | fairness-accuracy trade-off | Mixed |
| Use Fair MIP Forest instead of Agarwal | HSLS · HSLS | accuracy at higher FNR difference | Helped |
| Use Agarwal instead of Fair MIP Forest | HSLS · HSLS | FNR difference: close to 0.01 | Helped |
| Choose mean versus k-NN imputation for a fair learning method | Fair learning methods on Adult and COMPAS · Adult and COMPAS | fairness-accuracy trade-off | Mixed |
| Train an ensemble of multiple early-terminated trees instead of optimizing a single tree for longer | Fair MIP Forest · COMPAS and Adult | training time: more than 8x | Helped |
Closest papers by meaning
- Adapting Fairness Interventions to Missing Values (NeurIPS 2023 · similarity 0.93)
- The Importance of Modeling Data Missingness in Algorithmic Fairness: A Causal Perspective (AAAI 2021 · similarity 0.90)
- Fairness without Harm: An Influence-Guided Active Sampling Approach (NeurIPS 2024 · similarity 0.89)
- Enhancing Group Fairness in Online Settings Using Oblique Decision Forests (ICLR 2024 · similarity 0.88)
- Achievable Fairness on Your Data With Utility Guarantees (NeurIPS 2024 · similarity 0.87)
- FairNet: Dynamic Fairness Correction without Performance Loss via Contrastive Conditional LoRA (NeurIPS 2025 · similarity 0.86)
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