mlsanityData to 7 Oct 2026

FairCauseSyn: Towards Causally Fair LLM-Augmented Synthetic Data Generation

arXiv 2506.19082 ↗First posted 23 Jun 2025cs.LG cs.AIcited by 3

On the map: Synthetic data and self-improvement for LLMs → Privacy-preserving tabular data synthesis

Abstract (arXiv)

Synthetic data generation creates data based on real-world data using generative models. In health applications, generating high-quality data while maintaining fairness for sensitive attributes is essential for equitable outcomes. Existing GAN-based and LLM-based methods focus on counterfactual fairness and are primarily applied in finance and legal domains. Causal fairness provides a more comprehensive evaluation framework by preserving causal structure, but current synthetic data generation methods do not address it in health settings. To fill this gap, we develop the first LLM-augmented synthetic data generation method to enhance causal fairness using real-world tabular health data. Our generated data deviates by less than 10% from real data on causal fairness metrics. When trained on causally fair predictors, synthetic data reduces bias on the sensitive attribute by 70% compared to real data. This work improves access to fair synthetic data, supporting equitable health research and healthcare delivery.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Placed in Privacy-preserving tabular data synthesis, one of 13 directions in Synthetic data and self-improvement for LLMs. Builds on 17 of its 29 references that are in the map. First posted on arXiv 23 Jun 2025.

Builds on

17 of 29 references.

References · 17

  1. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
  2. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
  3. 2017Counterfactual Fairnesscited by 802
  4. 2022A Survey on In-context Learningcited by 717
  5. 2019Modeling Tabular data using Conditional GANcited by 695
  6. 2022TabDDPM: Modelling Tabular Data with Diffusion Modelscited by 373
  7. 2022Language Models are Realistic Tabular Data Generatorscited by 290
  8. 2018Path-Specific Counterfactual Fairnesscited by 179
  9. 2018FairGAN: Fairness-aware Generative Adversarial Networkscited by 164
  10. 2021DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networkscited by 109
  11. 2023REaLTabFormer: Generating Realistic Relational and Tabular Data using Transformerscited by 93
  12. 2022Synthetic Data -- what, why and how?cited by 66
5 more
  1. 2018Learning Optimal Fair Policiescited by 49
  2. 2023SGP-TOD: Building Task Bots Effortlessly via Schema-Guided LLM Promptingcited by 39
  3. 2021TabFairGAN: Fair Tabular Data Generation with Generative Adversarial Networkscited by 34
  4. 2021fairadapt: Causal Reasoning for Fair Data Pre-processingcited by 16
  5. 2023Causal Fairness under Unobserved Confounding: A Neural Sensitivity Frameworkcited by 10

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026A Survey on Evaluating Quality and Trustworthiness in LLM-Generated Datacited by 5
  2. 2026Memisis: Orchestrating and Evaluating Synthetic Data for Tabular Health Datasetscited by 0
  3. 2026Portable Causal Fairness Across Synthetic Data Generator Familiescited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025AIM-Fair: Advancing Algorithmic Fairness via Selectively Fine-Tuning Biased Models with Contextual Synthetic Data
  2. 2026Generative Synthetic Data for Causal Inference: Pitfalls, Remedies, and Opportunities
  3. 2025A Technical Exploration of Causal Inference with Hybrid LLM Synthetic Data
  4. 2024Generating Synthetic Fair Syntax-agnostic Data by Learning and Distilling Fair Representation
  5. 2026Causal Pre-training Under the Fairness Lens: An Empirical Study of TabPFN
  6. 2024FairDiffusion: Enhancing Equity in Latent Diffusion Models via Fair Bayesian Perturbation
  7. 2025Achieving Hilbert-Schmidt Independence Under R'enyi Differential Privacy for Fair and Private Data Generation
  8. 2024Balanced Mixed-Type Tabular Data Synthesis with Diffusion Models