mlsanityData to 14 Oct 2026

DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks

arXiv 2110.12884 ↗First posted 25 Oct 2021cs.LG stat.MLcited by 109

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Abstract (arXiv)

Machine learning models have been criticized for reflecting unfair biases in the training data. Instead of solving for this by introducing fair learning algorithms directly, we focus on generating fair synthetic data, such that any downstream learner is fair. Generating fair synthetic data from unfair data - while remaining truthful to the underlying data-generating process (DGP) - is non-trivial. In this paper, we introduce DECAF: a GAN-based fair synthetic data generator for tabular data. With DECAF we embed the DGP explicitly as a structural causal model in the input layers of the generator, allowing each variable to be reconstructed conditioned on its causal parents. This procedure enables inference time debiasing, where biased edges can be strategically removed for satisfying user-defined fairness requirements. The DECAF framework is versatile and compatible with several popular definitions of fairness. In our experiments, we show that DECAF successfully removes undesired bias and - in contrast to existing methods - is capable of generating high-quality synthetic data. Furthermore, we provide theoretical guarantees on the generator's convergence and the fairness of downstream models.

Key facts. Cited by 109 papers in the map (data through 14 Oct 2026). Builds on 20 of its 48 references that are in the map. First posted on arXiv 25 Oct 2021.

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20 of 48 references.

References · 20

  1. 2013Auto-Encoding Variational Bayescited by 4,053
  2. 2017Improved Training of Wasserstein GANscited by 2,235
  3. 2016Equality of Opportunity in Supervised Learningcited by 2,007
  4. 2019Improved Precision and Recall Metric for Assessing Generative Modelscited by 944
  5. 2017Counterfactual Fairnesscited by 804
  6. 2014Certifying and removing disparate impactcited by 715
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  8. 2018Differentially Private Generative Adversarial Networkcited by 235
  9. 2018Gender Bias in Neural Natural Language Processingcited by 211
  10. 2021How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Modelscited by 196
  11. 2017Avoiding Discrimination through Causal Reasoningcited by 194
  12. 2020Fairness in Machine Learningcited by 194
8 more
  1. 2018FairGAN: Fairness-aware Generative Adversarial Networkscited by 164
  2. 2017CausalGAN: Learning Causal Implicit Generative Models with Adversarial Trainingcited by 152
  3. 2017Fair Inference On Outcomescited by 144
  4. 2019Fair Generative Modeling via Weak Supervisioncited by 114
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 10; 2023: 31; 2024: 27; 2025: 27; 2026: 14.

109 papers.

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  1. 2023Machine Learning for Synthetic Data Generation: A Reviewcited by 120
  2. 2023DCFace: Synthetic Face Generation with Dual Condition Diffusion Modelcited by 116
  3. 2023Synthcity: facilitating innovative use cases of synthetic data in different data modalitiescited by 76
  4. 2022Synthetic Data -- what, why and how?cited by 67
  5. 2023Membership Inference Attacks against Synthetic Data through Overfitting Detectioncited by 66
  6. 2024Balancing Act: Distribution-Guided Debiasing in Diffusion Modelscited by 52
  7. 2024Current state of LLM Risks and AI Guardrailscited by 46
  8. 2023Can You Rely on Your Model Evaluation? Improving Model Evaluation with Synthetic Test Datacited by 32
  9. 2023Beyond Privacy: Navigating the Opportunities and Challenges of Synthetic Datacited by 29
  10. 2023Synthetic data, real errors: how (not) to publish and use synthetic datacited by 29
  11. 2024How Realistic Is Your Synthetic Data? Constraining Deep Generative Models for Tabular Datacited by 29
  12. 2024ClavaDDPM: Multi-relational Data Synthesis with Cluster-guided Diffusion Modelscited by 28
38 more
  1. 2024SynthEval: A Framework for Detailed Utility and Privacy Evaluation of Tabular Synthetic Datacited by 27
  2. 2024Can Knowledge Graphs Make Large Language Models More Trustworthy? An Empirical Study Over Open-ended Question Answeringcited by 24
  3. 2023Reimagining Synthetic Tabular Data Generation through Data-Centric AI: A Comprehensive Benchmarkcited by 23
  4. 2023Causal Deep Learningcited by 20
  5. 2025Beyond the convexity assumption: Realistic tabular data generation under quantifier-free real linear constraintscited by 18
  6. 2023SurvivalGAN: Generating Time-to-Event Data for Survival Analysiscited by 17
  7. 2022Conditional Generation of Medical Time Series for Extrapolation to Underrepresented Populationscited by 14
  8. 2022DC-Check: A Data-Centric AI checklist to guide the development of reliable machine learning systemscited by 14
  9. 2023CHeart: A Conditional Spatio-Temporal Generative Model for Cardiac Anatomycited by 13
  10. 2023Auditing and Generating Synthetic Data with Controllable Trust Trade-offscited by 13
  11. 2023SaGess: Sampling Graph Denoising Diffusion Model for Scalable Graph Generationcited by 13
  12. 2023CuTS: Customizable Tabular Synthetic Data Generationcited by 13
  13. 2024The Pursuit of Fairness in Artificial Intelligence Models: A Surveycited by 13
  14. 2025A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMscited by 13
  15. 2023On the Usefulness of Synthetic Tabular Data Generationcited by 12
  16. 2024Balanced Mixed-Type Tabular Data Synthesis with Diffusion Modelscited by 12
  17. 2025Position: All Current Generative Fidelity and Diversity Metrics are Flawedcited by 12
  18. 2023No Fair Lunch: A Causal Perspective on Dataset Bias in Machine Learning for Medical Imagingcited by 11
  19. 2023Emerging Synergies in Causality and Deep Generative Models: A Surveycited by 10
  20. 2024Modular Learning of Deep Causal Generative Models for High-dimensional Causal Inferencecited by 9
  21. 2023Synthetic Data in Healthcarecited by 8
  22. 2024Learning Structural Causal Models through Deep Generative Models: Methods, Guarantees, and Challengescited by 8
  23. 2024FairGen: Enhancing Fairness in Text-to-Image Diffusion Models via Self-Discovering Latent Directionscited by 8
  24. 2023Achieving Counterfactual Fairness for Anomaly Detectioncited by 7
  25. 2023Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Toolscited by 7
  26. 2024LLM4GRN: Discovering Causal Gene Regulatory Networks with LLMs -- Evaluation through Synthetic Data Generationcited by 7
  27. 2022CAT: Controllable Attribute Translation for Fair Facial Attribute Classificationcited by 6
  28. 2023Strong statistical parity through fair synthetic datacited by 6
  29. 2024Fairness-Aware Meta-Learning via Nash Bargainingcited by 6
  30. 2022Breaking the Spurious Causality of Conditional Generation via Fairness Intervention with Corrective Samplingcited by 5
  31. 2024VIGFace: Virtual Identity Generation for Privacy-Free Face Recognitioncited by 5
  32. 2025Metrics for Inter-Dataset Similarity with Example Applications in Synthetic Data and Feature Selection Evaluation -- Extended Versioncited by 5
  33. 2025Diffusion Transformers for Tabular Data Time Series Generationcited by 5
  34. 2025New Money: A Systematic Review of Synthetic Data Generation for Financecited by 5
  35. 2023Consistent End-to-End Estimation for Counterfactual Fairnesscited by 4
  36. 2024Bt-GAN: Generating Fair Synthetic Healthdata via Bias-transforming Generative Adversarial Networkscited by 4
  37. 2024FADE: Towards Fairness-aware Generation for Domain Generalization via Classifier-Guided Score-based Diffusion Modelscited by 4
  38. 2024Unmasking Trees for Tabular Datacited by 4

50 most cited of 109 All 109 (JSON)