# QUCE: The Minimisation and Quantification of Path-Based Uncertainty for Generative Counterfactual Explanations

arXiv: https://arxiv.org/abs/2402.17516 · HTML: https://mlsanity.com/paper/2402.17516/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 37 references that are in the map.
- First posted on arXiv 27 Feb 2024.

## Builds on (most cited first, 18 of 18)

- [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)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [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)
- [Reliable Post hoc Explanations: Modeling Uncertainty in Explainability](https://mlsanity.com/paper/2008.05030/) (2020)
- [Explaining Explanations: Axiomatic Feature Interactions for Deep Networks](https://mlsanity.com/paper/2002.04138/) (2020)
- [Guided Integrated Gradients: An Adaptive Path Method for Removing Noise](https://mlsanity.com/paper/2106.09788/) (2021)
- [Getting a CLUE: A Method for Explaining Uncertainty Estimates](https://mlsanity.com/paper/2006.06848/) (2020)
- [CLEAR: Generative Counterfactual Explanations on Graphs](https://mlsanity.com/paper/2210.08443/) (2022)
- [A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When?](https://mlsanity.com/paper/2304.04780/) (2023)
- [IDGI: A Framework to Eliminate Explanation Noise from Integrated Gradients](https://mlsanity.com/paper/2303.14242/) (2023)
- [Uncertainty Quantification using Variational Inference for Biomedical Image Segmentation](https://mlsanity.com/paper/2008.07588/) (2020)
- [Negative Flux Aggregation to Estimate Feature Attributions](https://mlsanity.com/paper/2301.06989/) (2023)
- [Reconstruction Error-based Anomaly Detection with Few Outlying Examples](https://mlsanity.com/paper/2305.10464/) (2023)
- [Towards credible visual model interpretation with path attribution](https://mlsanity.com/paper/2305.14395/) (2023)
- [Hidden Conflicts in Neural Networks and Their Implications for Explainability](https://mlsanity.com/paper/2310.20363/) (2023)
- [On Understanding the Influence of Controllable Factors with a Feature Attribution Algorithm: a Medical Case Study](https://mlsanity.com/paper/2203.12701/) (2022)

## Built on by (most cited first, 4 of 4)

- [Uncertainty Quantification as a Principled Foundation for Explainable Artificial Intelligence: A Case Study of Counterfactual Explanations](https://mlsanity.com/paper/2502.17007/) (2025)
- [When Explanations Compete: Policy-Aware Selection Under Uncertainty](https://mlsanity.com/paper/2410.05479/) (2024)
- [Tabular Diffusion Counterfactual Explanations](https://mlsanity.com/paper/2509.00876/) (2025)
- [AVCG: A Generalized Variational Framework for Counterfactual Generation under Hypothesis Distributions](https://mlsanity.com/paper/2609.07917/) (2026)

## Abstract (arXiv)

Deep Neural Networks (DNNs) stand out as one of the most prominent approaches within the Machine Learning (ML) domain. The efficacy of DNNs has surged alongside recent increases in computational capacity, allowing these approaches to scale to significant complexities for addressing predictive challenges in big data. However, as the complexity of DNN models rises, interpretability diminishes. In response to this challenge, explainable models such as Adversarial Gradient Integration (AGI) leverage path-based gradients provided by DNNs to elucidate their decisions. Yet the performance of path-based explainers can be compromised when gradients exhibit irregularities during out-of-distribution path traversal. In this context, we introduce Quantified Uncertainty Counterfactual Explanations (QUCE), a method designed to mitigate out-of-distribution traversal by minimizing path uncertainty. QUCE not only quantifies uncertainty when presenting explanations but also generates more certain counterfactual examples. We showcase the performance of the QUCE method by comparing it with competing methods for both path-based explanations and generative counterfactual examples.