TabChange: Precise Attribute Changes in Tabular Data

2026-05-29

AI Summary

This work addresses the challenge that naively altering attributes in tabular data often disrupts their natural interdependencies, yielding implausible counterfactual samples. To mitigate this, the authors propose a precision-editing approach that adaptively adjusts modifications based on attribute relationship strength: weakly correlated attributes are directly flipped, while strongly correlated ones undergo explicit removal of target attribute information in latent space via adversarial learning, thereby avoiding the over-modification caused by residual attribute signals in existing methods. Integrating a conditional variational autoencoder with an attribute disentanglement mechanism, the proposed method generates counterfactuals across seven datasets that closely resemble original instances, preserve data plausibility, significantly increase the proportion of valid counterfactuals, and reduce the generation of invalid samples.

Abstract

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.

Problem

• tabular data
• attribute editing
• counterfactual generation
• naturalness
• minimal change

Innovation

• TabChange
• counterfactual generation
• attribute editing
• adversarial learning
• tabular data