# CounteRGAN: Generating Realistic Counterfactuals with Residual Generative Adversarial Nets

arXiv: https://arxiv.org/abs/2009.05199 · HTML: https://mlsanity.com/paper/2009.05199/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2020.

## Built on by (most cited first, 29 of 29)

- [If Only We Had Better Counterfactual Explanations: Five Key Deficits to Rectify in the Evaluation of Counterfactual XAI Techniques](https://mlsanity.com/paper/2103.01035/) (2021)
- [Diffusion Models for Counterfactual Explanations](https://mlsanity.com/paper/2203.15636/) (2022)
- [Adversarial Counterfactual Visual Explanations](https://mlsanity.com/paper/2303.09962/) (2023)
- [VCNet: A self-explaining model for realistic counterfactual generation](https://mlsanity.com/paper/2212.10847/) (2022)
- [PRISM: High-Resolution & Precise Counterfactual Medical Image Generation using Language-guided Stable Diffusion](https://mlsanity.com/paper/2503.00196/) (2025)
- [Designing Counterfactual Generators using Deep Model Inversion](https://mlsanity.com/paper/2109.14274/) (2021)
- [Debiasing Counterfactuals In the Presence of Spurious Correlations](https://mlsanity.com/paper/2308.10984/) (2023)
- [Generating Sparse Counterfactual Explanations For Multivariate Time Series](https://mlsanity.com/paper/2206.00931/) (2022)
- [Emerging Synergies in Causality and Deep Generative Models: A Survey](https://mlsanity.com/paper/2301.12351/) (2023)
- [Inherently Interpretable Multi-Label Classification Using Class-Specific Counterfactuals](https://mlsanity.com/paper/2303.00500/) (2023)
- [A Framework for Interpretability in Machine Learning for Medical Imaging](https://mlsanity.com/paper/2310.01685/) (2023)
- [Modular Learning of Deep Causal Generative Models for High-dimensional Causal Inference](https://mlsanity.com/paper/2401.01426/) (2024)
- [Counterfactual Concept Bottleneck Models](https://mlsanity.com/paper/2402.01408/) (2024)
- [Principled Knowledge Extrapolation with GANs](https://mlsanity.com/paper/2205.13444/) (2022)
- [AURA: A Multi-Modal Medical Agent for Understanding, Reasoning & Annotation](https://mlsanity.com/paper/2507.16940/) (2025)
- [Explaining Image Classifiers Using Contrastive Counterfactuals in Generative Latent Spaces](https://mlsanity.com/paper/2206.05257/) (2022)
- [Combining Counterfactuals With Shapley Values To Explain Image Models](https://mlsanity.com/paper/2206.07087/) (2022)
- [GLANCE: Global to Local Architecture-Neutral Concept-based Explanations](https://mlsanity.com/paper/2207.01917/) (2022)
- [Hierarchical Symbolic Reasoning in Hyperbolic Space for Deep Discriminative Models](https://mlsanity.com/paper/2207.01916/) (2022)
- [MACAW: A Causal Generative Model for Medical Imaging](https://mlsanity.com/paper/2412.02900/) (2024)
- [Actionable Recourse via GANs for Mobile Health](https://mlsanity.com/paper/2211.06525/) (2022)
- [Graph Community Augmentation with GMM-based Modeling in Latent Space](https://mlsanity.com/paper/2412.01163/) (2024)
- [Leveraging Vision-Language Foundation Models to Reveal Hidden Image-Attribute Relationships in Medical Imaging](https://mlsanity.com/paper/2503.23618/) (2025)
- [When the Edit Changes the Patient: Measuring Identity Preservation in Counterfactual Retinal Images](https://mlsanity.com/paper/2608.23024/) (2026)
- [DA-DGCEx: Ensuring Validity of Deep Guided Counterfactual Explanations With Distribution-Aware Autoencoder Loss](https://mlsanity.com/paper/2104.09062/) (2021)
- [Personalized Path Recourse for Reinforcement Learning Agents](https://mlsanity.com/paper/2312.08724/) (2023)
- [Generalizability vs. Counterfactual Explainability Trade-Off](https://mlsanity.com/paper/2505.23225/) (2025)
- [An Explainable Gaussian Process Auto-encoder for Tabular Data](https://mlsanity.com/paper/2509.00884/) (2025)
- [Residual-Space Evolutionary Optimization via Flow-based Generative Models](https://mlsanity.com/paper/2606.20084/) (2026)

## Abstract (arXiv)

The prevalence of machine learning models in various industries has led to growing demands for model interpretability and for the ability to provide meaningful recourse to users. For example, patients hoping to improve their diagnoses or loan applicants seeking to increase their chances of approval. Counterfactuals can help in this regard by identifying input perturbations that would result in more desirable prediction outcomes. Meaningful counterfactuals should be able to achieve the desired outcome, but also be realistic, actionable, and efficient to compute. Current approaches achieve desired outcomes with moderate actionability but are severely limited in terms of realism and latency. To tackle these limitations, we apply Generative Adversarial Nets (GANs) toward counterfactual search. We also introduce a novel Residual GAN (RGAN) that helps to improve counterfactual realism and actionability compared to regular GANs. The proposed CounteRGAN method utilizes an RGAN and a target classifier to produce counterfactuals capable of providing meaningful recourse. Evaluations on two popular datasets highlight how the CounteRGAN is able to overcome the limitations of existing methods, including latency improvements of >50x to >90,000x, making meaningful recourse available in real-time and applicable to a wide range of domains.