[Paper] On the Robustness of Fairness Practices: A Causal Framework for Systematic Evaluation

Source: arXiv - 2601.03621v1

Overview

The paper “On the Robustness of Fairness Practices: A Causal Framework for Systematic Evaluation” asks a question that every ML engineer eventually faces: Can we trust the fairness tricks we’ve been taught when the data we work with is messy, biased, or changing? By marrying causal reasoning with empirical testing, the authors provide a systematic way to stress‑test popular fairness interventions (e.g., adding sensitive attributes, feature selection, bias‑mitigation algorithms) under realistic data problems such as label noise, missing values, and distribution shift.

Key Contributions

• Causal evaluation framework – Introduces a unified causal graph model that captures how data collection, preprocessing, and model training interact with fairness outcomes.
• Robustness taxonomy – Defines three orthogonal axes of data imperfection (faulty labels, missing data, covariate shift) and maps each fairness practice onto this space.
• Systematic benchmarking suite – Builds an open‑source toolkit (available on GitHub) that automatically injects controlled imperfections into benchmark datasets (e.g., Adult, COMPAS) and measures the impact on a range of fairness metrics (DP, EO, AUC‑DP, etc.).
• Empirical insights – Shows that many widely‑adopted interventions (e.g., re‑weighting, adversarial debiasing) are fragile under modest label noise, while simple “sensitive‑feature‑inclusion” remains surprisingly stable.
• Guidelines for practitioners – Provides a decision matrix that helps engineers pick the most robust fairness technique given the known data quality issues of their project.

Methodology

1. Causal Modeling – The authors start by drawing a structural causal model (SCM) that links raw data generation → pre‑processing → model training → prediction. Sensitive attributes (e.g., gender, race) and potential confounders are explicitly represented as nodes, allowing the use of do‑calculus to reason about “what would happen if we intervene on the training pipeline.”

2. Perturbation Engine – Using the SCM, they programmatically introduce three types of imperfections:

   • Label noise: flip a configurable percentage of ground‑truth labels.
   • Missingness: randomly mask features or apply Missing‑Not‑At‑Random (MNAR) patterns correlated with the sensitive attribute.
   • Distribution shift: replace a fraction of the test set with samples drawn from a shifted covariate distribution (e.g., different income brackets).

3. Fairness Interventions Tested – Six representative practices from the literature:

   • Sensitive‑feature inclusion (SFI)
   • Feature removal (FR)
   • Pre‑processing re‑weighting (RW)
   • Pre‑processing disparate impact remover (DIR)
   • In‑processing adversarial debiasing (AD)
   • Post‑processing calibrated equalized odds (CEO)

4. Evaluation Protocol – For each dataset‑intervention‑perturbation combination, they compute:

   • Predictive performance (accuracy / AUC)
   • Four fairness metrics (Demographic Parity, Equalized Odds, Predictive Parity, Calibration)
   • Robustness scores (area under the performance‑fairness curve as perturbation severity increases).

5. Statistical Analysis – Paired t‑tests and bootstrapped confidence intervals are used to assess whether observed degradations are statistically significant.

Results & Findings

Key take‑aways

• No “one‑size‑fits‑all”: an intervention that shines under clean data can crumble under modest noise.
• Simplicity often wins: merely keeping the sensitive attribute in the model (SFI) provides a surprisingly stable fairness baseline across all perturbations.
• In‑processing methods are the most fragile because they tightly couple fairness constraints with the learned representation, which becomes unstable when the data distribution changes.
• Post‑processing calibrations (e.g., CEO) are the most resilient to covariate shift but can sacrifice a bit of overall accuracy.

Practical Implications

1. Data‑quality checklist before fairness engineering – Teams should first quantify label reliability, missingness patterns, and potential distribution shifts. The paper’s toolkit can automate this audit.

2. Prioritize robust interventions – If the pipeline is expected to encounter noisy labels (common in crowdsourced or legacy datasets), start with SFI or simple re‑weighting before moving to sophisticated adversarial methods.

3. Deployability – Post‑processing methods like calibrated equalized odds can be added as a “fairness shim” after the model is trained, making them easier to roll out in CI/CD pipelines without retraining.

4. Monitoring in production – The causal framework suggests monitoring not just model accuracy but also the causal pathways (e.g., drift in the distribution of sensitive attributes). Alerts can trigger a re‑evaluation of the fairness intervention chosen.

5. Regulatory compliance – By providing a systematic robustness report (e.g., “fairness holds up to 8 % label noise”), organizations can better demonstrate due‑diligence to auditors and regulators.

Limitations & Future Work

• Scope of datasets – Experiments focus on classic tabular fairness benchmarks (Adult, COMPAS, German Credit). Results may differ for high‑dimensional domains such as vision or speech.
• Synthetic perturbations – While the perturbation engine is grounded in causal theory, real‑world data issues (e.g., systematic bias in data collection pipelines) can be more complex than the simulated noise/missingness patterns used.
• Limited fairness metrics – The study evaluates four widely‑used metrics; emerging notions like individual fairness or counterfactual fairness are not covered.
• Future directions – Extending the framework to multi‑task or continual‑learning settings, integrating automated causal discovery to tailor the SCM to a given dataset, and building a dashboard that visualizes robustness trade‑offs in real time.

Bottom line: This work equips ML engineers with a causal lens and a practical toolbox to ask—and answer—“Will my fairness fix survive the messiness of real data?” By foregrounding robustness, it pushes fairness from a one‑off checklist item to a continuously monitored system property.

Authors

• Verya Monjezi
• Ashish Kumar
• Ashutosh Trivedi
• Gang Tan
• Saeid Tizpaz-Niari

Paper Information

• arXiv ID: 2601.03621v1
• Categories: cs.SE
• Published: January 7, 2026
• PDF: Download PDF