# CARE: Coherent Actionable Recourse based on Sound Counterfactual Explanations

arXiv: https://arxiv.org/abs/2108.08197 · HTML: https://mlsanity.com/paper/2108.08197/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 34 references that are in the map.
- First posted on arXiv 18 Aug 2021.

## Builds on (most cited first, 14 of 14)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [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)
- [Actionable Recourse in Linear Classification](https://mlsanity.com/paper/1809.06514/) (2018)
- [Algorithmic Recourse: from Counterfactual Explanations to Interventions](https://mlsanity.com/paper/2002.06278/) (2020)
- [FACE: Feasible and Actionable Counterfactual Explanations](https://mlsanity.com/paper/1909.09369/) (2019)
- [Interpretable Counterfactual Explanations Guided by Prototypes](https://mlsanity.com/paper/1907.02584/) (2019)
- [Multi-Objective Counterfactual Explanations](https://mlsanity.com/paper/2004.11165/) (2020)
- [Learning Model-Agnostic Counterfactual Explanations for Tabular Data](https://mlsanity.com/paper/1910.09398/) (2019)
- [Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers](https://mlsanity.com/paper/1912.03277/) (2019)
- [Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems](https://mlsanity.com/paper/1907.09615/) (2019)
- [The Dangers of Post-hoc Interpretability: Unjustified Counterfactual Explanations](https://mlsanity.com/paper/1907.09294/) (2019)
- [Issues with post-hoc counterfactual explanations: a discussion](https://mlsanity.com/paper/1906.04774/) (2019)
- [BRPO: Batch Residual Policy Optimization](https://mlsanity.com/paper/2002.05522/) (2020)

## Built on by (most cited first, 17 of 17)

- [Transport-based Counterfactual Models](https://mlsanity.com/paper/2108.13025/) (2021)
- [Achieving Diversity in Counterfactual Explanations: a Review and Discussion](https://mlsanity.com/paper/2305.05840/) (2023)
- [Towards Unifying Evaluation of Counterfactual Explanations: Leveraging Large Language Models for Human-Centric Assessments](https://mlsanity.com/paper/2410.21131/) (2024)
- [MCCE: Monte Carlo sampling of realistic counterfactual explanations](https://mlsanity.com/paper/2111.09790/) (2021)
- [A multi-criteria approach for selecting an explanation from the set of counterfactuals produced by an ensemble of explainers](https://mlsanity.com/paper/2403.13940/) (2024)
- [Enhancing Counterfactual Explanation Search with Diffusion Distance and Directional Coherence](https://mlsanity.com/paper/2404.12810/) (2024)
- [Reinforcement Learning for Durable Algorithmic Recourse](https://mlsanity.com/paper/2509.22102/) (2025)
- [Flexible Counterfactual Explanations with Generative Models](https://mlsanity.com/paper/2502.17613/) (2025)
- [Predicting Satisfaction of Counterfactual Explanations from Human Ratings of Explanatory Qualities](https://mlsanity.com/paper/2504.13899/) (2025)
- [DiCoFlex: Model-agnostic diverse counterfactuals with flexible control](https://mlsanity.com/paper/2505.23700/) (2025)
- [T-COL: Generating Counterfactual Explanations for General User Preferences on Variable Machine Learning Systems](https://mlsanity.com/paper/2309.16146/) (2023)
- [Learning Actionable Counterfactual Explanations in Large State Spaces](https://mlsanity.com/paper/2404.17034/) (2024)
- [Towards Meaningful Transparency in Civic AI Systems](https://mlsanity.com/paper/2510.07889/) (2025)
- [Beyond Satisfaction: From Placebic to Actionable Explanations For Enhanced Understandability](https://mlsanity.com/paper/2512.06591/) (2025)
- [GradCFA: A Hybrid Gradient-Based Counterfactual and Feature Attribution Explanation Algorithm for Local Interpretation of Neural Networks](https://mlsanity.com/paper/2603.15373/) (2026)
- [Class-Aware Reinforcement Learning for Counterfactual Explanation Generation](https://mlsanity.com/paper/2607.27905/) (2026)
- [FCA-Guided Counterfactual Explanations for Multi-Modal Breast Cancer Diagnosis: A Framework Achieving Perfect Validity with Emergent Sparsity](https://mlsanity.com/paper/2609.20067/) (2026)

## Abstract (arXiv)

Counterfactual explanation methods interpret the outputs of a machine learning model in the form of "what-if scenarios" without compromising the fidelity-interpretability trade-off. They explain how to obtain a desired prediction from the model by recommending small changes to the input features, aka recourse. We believe an actionable recourse should be created based on sound counterfactual explanations originating from the distribution of the ground-truth data and linked to the domain knowledge. Moreover, it needs to preserve the coherency between changed/unchanged features while satisfying user/domain-specified constraints. This paper introduces CARE, a modular explanation framework that addresses the model- and user-level desiderata in a consecutive and structured manner. We tackle the existing requirements by proposing novel and efficient solutions that are formulated in a multi-objective optimization framework. The designed framework enables including arbitrary requirements and generating counterfactual explanations and actionable recourse by choice. As a model-agnostic approach, CARE generates multiple, diverse explanations for any black-box model in tabular classification and regression settings. Several experiments on standard data sets and black-box models demonstrate the effectiveness of our modular framework and its superior performance compared to the baselines.