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TabChange: Precise Attribute Changes in Tabular Data

arXiv 2606.00503 ↗First posted 30 May 2026cs.LG cs.AIcited by 0

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Abstract (arXiv)

Modifying an attribute in tabular data often introduces an unnatural instance by breaking its relationships with other attributes. The modified instance must be both natural and minimally changed from the original instance. This paper addresses the challenge of generating such a modified instance. We identify key limitations in existing approaches: generative models either don't support instance-level attribute editing or, in the case of methods like CVAE, retain attribute information in the latent space, leading to unnecessary modifications. To solve this, we propose TabChange, an approach that analyzes the relationship between the attribute of interest and other attributes in the dataset. If the relationship is weak, it simply flips the attribute; if it is strong, it uses an adversarial framework that removes information about the attribute in the latent space representation. This removal enables precise modifications, making only the necessary adjustments to maintain naturalness. Our experiments across seven datasets show that TabChange generates counterfactuals in attributes that are comparable in naturalness and are more proximal to their original instances. This leads to a higher number of valid counterfactuals and a lower number of invalid counterfactuals compared to the baselines.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 46 references that are in the map. First posted on arXiv 30 May 2026.

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18 of 46 references.

References · 18

  1. 2019Modeling Tabular data using Conditional GANcited by 699
  2. 2019Explaining Machine Learning Classifiers through Diverse Counterfactual Explanationscited by 614
  3. 2022TabDDPM: Modelling Tabular Data with Diffusion Modelscited by 377
  4. 2018Learning Adversarially Fair and Transferable Representationscited by 353
  5. 2018Actionable Recourse in Linear Classificationcited by 328
  6. 2015Censoring Representations with an Adversarycited by 192
  7. 2018Aequitas: A Bias and Fairness Audit Toolkitcited by 147
  8. 2019Learning Model-Agnostic Counterfactual Explanations for Tabular Datacited by 146
  9. 2019Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systemscited by 131
  10. 2017Fader Networks: Manipulating Images by Sliding Attributescited by 118
  11. 2021DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networkscited by 109
  12. 2020Counterfactual Fairness with Disentangled Causal Effect Variational Autoencodercited by 45
6 more
  1. 2023Latent Imitator: Generating Natural Individual Discriminatory Instances for Black-Box Fairness Testingcited by 23
  2. 2021Automatic Fairness Testing of Neural Classifiers through Adversarial Samplingcited by 14
  3. 2024Balanced Mixed-Type Tabular Data Synthesis with Diffusion Modelscited by 12
  4. 2024TABCF: Counterfactual Explanations for Tabular Data Using a Transformer-Based VAEcited by 8
  5. 2023Navigating the Structured What-If Spaces: Counterfactual Generation via Structured Diffusioncited by 5
  6. 2025From Search To Sampling: Generative Models For Robust Algorithmic Recoursecited by 5

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