# Counterfactual Explanations for Machine Learning: Challenges Revisited

arXiv: https://arxiv.org/abs/2106.07756 · HTML: https://mlsanity.com/paper/2106.07756/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 35 references that are in the map.
- First posted on arXiv 14 Jun 2021.

## Builds on (most cited first, 15 of 15)

- [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016)
- [Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR](https://mlsanity.com/paper/1711.00399/) (2017)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Actionable Recourse in Linear Classification](https://mlsanity.com/paper/1809.06514/) (2018)
- [Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives](https://mlsanity.com/paper/1802.07623/) (2018)
- [Improving fairness in machine learning systems: What do industry practitioners need?](https://mlsanity.com/paper/1812.05239/) (2018)
- [Algorithmic Recourse: from Counterfactual Explanations to Interventions](https://mlsanity.com/paper/2002.06278/) (2020)
- [Model-Agnostic Counterfactual Explanations for Consequential Decisions](https://mlsanity.com/paper/1905.11190/) (2019)
- [Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers](https://mlsanity.com/paper/1912.03277/) (2019)
- [Algorithmic recourse under imperfect causal knowledge: a probabilistic approach](https://mlsanity.com/paper/2006.06831/) (2020)
- [Benchmarking and Survey of Explanation Methods for Black Box Models](https://mlsanity.com/paper/2102.13076/) (2021)
- [A survey of algorithmic recourse: definitions, formulations, solutions, and prospects](https://mlsanity.com/paper/2010.04050/) (2020)
- [The Hidden Assumptions Behind Counterfactual Explanations and Principal Reasons](https://mlsanity.com/paper/1912.04930/) (2019)
- [Runaway Feedback Loops in Predictive Policing](https://mlsanity.com/paper/1706.09847/) (2017)
- [Ethical Dimensions of Visualization Research](https://mlsanity.com/paper/1811.07271/) (2018)

## Built on by (most cited first, 19 of 19)

- [Redefining Counterfactual Explanations for Reinforcement Learning: Overview, Challenges and Opportunities](https://mlsanity.com/paper/2210.11846/) (2022)
- [Counterfactual Explanations for Misclassified Images: How Human and Machine Explanations Differ](https://mlsanity.com/paper/2212.08733/) (2022)
- [Interpretation of Black Box NLP Models: A Survey](https://mlsanity.com/paper/2203.17081/) (2022)
- [A Survey on XAI for 5G and Beyond Security: Technical Aspects, Challenges and Research Directions](https://mlsanity.com/paper/2204.12822/) (2022)
- [Preemptively Pruning Clever-Hans Strategies in Deep Neural Networks](https://mlsanity.com/paper/2304.05727/) (2023)
- [View-based Explanations for Graph Neural Networks](https://mlsanity.com/paper/2401.02086/) (2024)
- [MCCE: Monte Carlo sampling of realistic counterfactual explanations](https://mlsanity.com/paper/2111.09790/) (2021)
- [RACCER: Towards Reachable and Certain Counterfactual Explanations for Reinforcement Learning](https://mlsanity.com/paper/2303.04475/) (2023)
- [Survey on AI Ethics: A Socio-technical Perspective](https://mlsanity.com/paper/2311.17228/) (2023)
- [Guiding the generation of counterfactual explanations through temporal background knowledge for Predictive Process Monitoring](https://mlsanity.com/paper/2403.11642/) (2024)
- [Data and AI governance: Promoting equity, ethics, and fairness in large language models](https://mlsanity.com/paper/2508.03970/) (2025)
- [Evaluating counterfactual explanations using Pearl's counterfactual method](https://mlsanity.com/paper/2301.02499/) (2023)
- [Understanding User Preferences in Explainable Artificial Intelligence: A Survey and a Mapping Function Proposal](https://mlsanity.com/paper/2302.03180/) (2023)
- [T-COL: Generating Counterfactual Explanations for General User Preferences on Variable Machine Learning Systems](https://mlsanity.com/paper/2309.16146/) (2023)
- [Tabular Diffusion based Actionable Counterfactual Explanations for Network Intrusion Detection](https://mlsanity.com/paper/2507.17161/) (2025)
- [Context-Aware Visualization for Explainable AI Recommendations in Social Media: A Vision for User-Aligned Explanations](https://mlsanity.com/paper/2508.00674/) (2025)
- [Axiomatic Foundations of Counterfactual Explanations](https://mlsanity.com/paper/2602.04028/) (2026)
- [Beyond One-Size-Fits-All: Adapting Counterfactual Explanations to User Objectives](https://mlsanity.com/paper/2404.08721/) (2024)
- [Explaining Reinforcement Learning Decisions in Self-adaptive Systems](https://mlsanity.com/paper/2608.14620/) (2026)

## Abstract (arXiv)

Counterfactual explanations (CFEs) are an emerging technique under the umbrella of interpretability of machine learning (ML) models. They provide ``what if'' feedback of the form ``if an input datapoint were x' instead of x, then an ML model's output would be y' instead of y.'' Counterfactual explainability for ML models has yet to see widespread adoption in industry. In this short paper, we posit reasons for this slow uptake. Leveraging recent work outlining desirable properties of CFEs and our experience running the ML wing of a model monitoring startup, we identify outstanding obstacles hindering CFE deployment in industry.