# Portable Causal Fairness Across Synthetic Data Generator Families

arXiv: https://arxiv.org/abs/2609.03180 · HTML: https://mlsanity.com/paper/2609.03180/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 26 references that are in the map.
- First posted on arXiv 2 Sep 2026.

## Builds on (most cited first, 18 of 18)

- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Counterfactual Fairness](https://mlsanity.com/paper/1703.06856/) (2017)
- [Modeling Tabular data using Conditional GAN](https://mlsanity.com/paper/1907.00503/) (2019)
- [TabDDPM: Modelling Tabular Data with Diffusion Models](https://mlsanity.com/paper/2209.15421/) (2022)
- [Differential Privacy Has Disparate Impact on Model Accuracy](https://mlsanity.com/paper/1905.12101/) (2019)
- [DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks](https://mlsanity.com/paper/2110.12884/) (2021)
- [Winning the NIST Contest: A scalable and general approach to differentially private synthetic data](https://mlsanity.com/paper/2108.04978/) (2021)
- [A causal framework for discovering and removing direct and indirect discrimination](https://mlsanity.com/paper/1611.07509/) (2016)
- [Geographic Spines in the 2020 Census Disclosure Avoidance System](https://mlsanity.com/paper/2203.16654/) (2022)
- [PreFair: Privately Generating Justifiably Fair Synthetic Data](https://mlsanity.com/paper/2212.10310/) (2022)
- [CausalDiffTab: Mixed-Type Causal-Aware Diffusion for Tabular Data Generation](https://mlsanity.com/paper/2506.14206/) (2025)
- [FairCauseSyn: Towards Causally Fair LLM-Augmented Synthetic Data Generation](https://mlsanity.com/paper/2506.19082/) (2025)
- [TabSCM: A practical Framework for Generating Realistic Tabular Data](https://mlsanity.com/paper/2604.22337/) (2026)
- [Privacy-Preserving Fair Synthetic Tabular Data](https://mlsanity.com/paper/2503.02968/) (2025)
- [Achieving Hilbert-Schmidt Independence Under R'enyi Differential Privacy for Fair and Private Data Generation](https://mlsanity.com/paper/2508.21815/) (2025)
- [PrAda-GAN: A Private Adaptive Generative Adversarial Network with Bayes Network Structure](https://mlsanity.com/paper/2511.07997/) (2025)
- [Where to Intervene? Benchmarking Fairness-Aware Learning on Differentially Private Synthetic Tabular Data](https://mlsanity.com/paper/2607.07471/) (2026)

## Abstract (arXiv)

When a statistical agency or regulator releases synthetic data in place of sensitive records, it chooses the generator that produces the table, and can shape that generator so unfair pathways are absent. DECAF made this concrete on one non-private GAN: three fairness definitions become three sets of edge cuts on the generator's causal graph. Whether the mechanism belongs to DECAF, or to causal factorisation itself, was untested. We port all three definitions to nine generators from three unrelated families (marginals-based, GAN, and diffusion, each with differentially private variants), across three levels of formal privacy guarantee, over 2,520 matched-pair runs on Adult and COMPAS datasets. The mechanism transfers everywhere, and our new causal diffusion backbone yields the fairest release of any family we tested, at fidelity close to the marginals tier. Applying the cut barely moves fidelity, only costs a downstream classifier about 0.07 to 0.15 AUC on average, and adding privacy guarantees don't make the data less fair.