# DisCERN:Discovering Counterfactual Explanations using Relevance Features from Neighbourhoods

arXiv: https://arxiv.org/abs/2109.05800 · HTML: https://mlsanity.com/paper/2109.05800/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 20 references that are in the map.
- First posted on arXiv 13 Sep 2021.

## Builds on (most cited first, 9 of 9)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR](https://mlsanity.com/paper/1711.00399/) (2017)
- [Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations](https://mlsanity.com/paper/1905.07697/) (2019)
- [Metrics for Explainable AI: Challenges and Prospects](https://mlsanity.com/paper/1812.04608/) (2018)
- [Deep Learning for Case-Based Reasoning through Prototypes: A Neural Network that Explains Its Predictions](https://mlsanity.com/paper/1710.04806/) (2017)
- [Good Counterfactuals and Where to Find Them: A Case-Based Technique for Generating Counterfactuals for Explainable AI (XAI)](https://mlsanity.com/paper/2005.13997/) (2020)
- [A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems](https://mlsanity.com/paper/1811.11839/) (2018)

## Built on by (most cited first, 10 of 10)

- [Even if Explanations: Prior Work, Desiderata & Benchmarks for Semi-Factual XAI](https://mlsanity.com/paper/2301.11970/) (2023)
- [Generating collective counterfactual explanations in score-based classification via mathematical optimization](https://mlsanity.com/paper/2310.12822/) (2023)
- [This changes to that : Combining causal and non-causal explanations to generate disease progression in capsule endoscopy](https://mlsanity.com/paper/2212.02506/) (2022)
- [Outcome-Guided Counterfactuals for Reinforcement Learning Agents from a Jointly Trained Generative Latent Space](https://mlsanity.com/paper/2207.07710/) (2022)
- [Counterfactual Explanations of Neural Network-Generated Response Curves](https://mlsanity.com/paper/2304.04063/) (2023)
- [Towards Feasible Counterfactual Explanations: A Taxonomy Guided Template-based NLG Method](https://mlsanity.com/paper/2310.02019/) (2023)
- [Introducing User Feedback-based Counterfactual Explanations (UFCE)](https://mlsanity.com/paper/2403.00011/) (2024)
- [iSee: Advancing Multi-Shot Explainable AI Using Case-based Recommendations](https://mlsanity.com/paper/2408.12941/) (2024)
- [FLEX: Feature Importance from Layered Counterfactual Explanations](https://mlsanity.com/paper/2511.11891/) (2025)
- [Class-Aware Reinforcement Learning for Counterfactual Explanation Generation](https://mlsanity.com/paper/2607.27905/) (2026)

## Abstract (arXiv)

Counterfactual explanations focus on "actionable knowledge" to help end-users understand how a machine learning outcome could be changed to a more desirable outcome. For this purpose a counterfactual explainer needs to discover input dependencies that relate to outcome changes. Identifying the minimum subset of feature changes needed to action an output change in the decision is an interesting challenge for counterfactual explainers. The DisCERN algorithm introduced in this paper is a case-based counter-factual explainer. Here counterfactuals are formed by replacing feature values from a nearest unlike neighbour (NUN) until an actionable change is observed. We show how widely adopted feature relevance-based explainers (i.e. LIME, SHAP), can inform DisCERN to identify the minimum subset of "actionable features". We demonstrate our DisCERN algorithm on five datasets in a comparative study with the widely used optimisation-based counterfactual approach DiCE. Our results demonstrate that DisCERN is an effective strategy to minimise actionable changes necessary to create good counterfactual explanations.