# Latent-CF: A Simple Baseline for Reverse Counterfactual Explanations

arXiv: https://arxiv.org/abs/2012.09301 · HTML: https://mlsanity.com/paper/2012.09301/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Dec 2020.

## Built on by (most cited first, 11 of 11)

- [Convex optimization for actionable & plausible counterfactual explanations](https://mlsanity.com/paper/2105.07630/) (2021)
- [FASTER-CE: Fast, Sparse, Transparent, and Robust Counterfactual Explanations](https://mlsanity.com/paper/2210.06578/) (2022)
- [Counterfactual Explanations via Latent Space Projection and Interpolation](https://mlsanity.com/paper/2112.00890/) (2021)
- [What-If Explanations Over Time: Counterfactuals for Time Series Classification](https://mlsanity.com/paper/2603.27792/) (2026)
- [Identifying Spurious Correlations using Counterfactual Alignment](https://mlsanity.com/paper/2312.02186/) (2023)
- [Faithful Counterfactual Visual Explanations (FCVE)](https://mlsanity.com/paper/2501.06841/) (2025)
- [Counterfactual Explanation for Regression via Disentanglement in Latent Space](https://mlsanity.com/paper/2311.08228/) (2023)
- [DA-DGCEx: Ensuring Validity of Deep Guided Counterfactual Explanations With Distribution-Aware Autoencoder Loss](https://mlsanity.com/paper/2104.09062/) (2021)
- [Two-step counterfactual generation for OOD examples](https://mlsanity.com/paper/2302.05196/) (2023)
- [Counterfactual Explanation via Search in Gaussian Mixture Distributed Latent Space](https://mlsanity.com/paper/2307.13390/) (2023)
- [Explaining Low Perception Model Competency with High-Competency Counterfactuals](https://mlsanity.com/paper/2504.05254/) (2025)

## Abstract (arXiv)

In the environment of fair lending laws and the General Data Protection Regulation (GDPR), the ability to explain a model's prediction is of paramount importance. High quality explanations are the first step in assessing fairness. Counterfactuals are valuable tools for explainability. They provide actionable, comprehensible explanations for the individual who is subject to decisions made from the prediction. It is important to find a baseline for producing them. We propose a simple method for generating counterfactuals by using gradient descent to search in the latent space of an autoencoder and benchmark our method against approaches that search for counterfactuals in feature space. Additionally, we implement metrics to concretely evaluate the quality of the counterfactuals. We show that latent space counterfactual generation strikes a balance between the speed of basic feature gradient descent methods and the sparseness and authenticity of counterfactuals generated by more complex feature space oriented techniques.