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awesome-counterfactual-explanations
awesome counterfactual explanations
https://github.com/wangyongjie-ntu/awesome-counterfactual-explanations
- Counterfactuals and Causability in Explainable Artificial Intelligence: Theory, Algorithms, and Applications
- A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence
- Good Counterfactuals and Where to Find Them: A Case-Based Technique for Generating Counterfactuals for Explainable AI (XAI)
- Counterfactual Explanations for Machine Learning: A Review
- A survey of algorithmic recourse: definitions, formulations, solutions, and prospects
- On the computation of counterfactual explanations -- A survey
- Issues with post-hoc counterfactual explanations: a discussion
- Counterfactuals in Explainable Artificial Intelligence (XAI): Evidence from Human Reasoning
- A Few Good Counterfactuals: Generating Interpretable, Plausible & Diverse Counterfactual Explanations
- GeCo: Quality Counterfactual Explanations in Real Time
- Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties - by-minimizing-uncertainty)
- FIMAP: Feature Importance by Minimal Adversarial Perturbation
- Generate Your Counterfactuals: Towards Controlled Counterfactual Generation for Text
- Counterfactual Fairness with Disentangled Causal Effect Variational Autoencoder
- Counterfactual Explanations for Oblique Decision Trees: Exact, Efficient Algorithms
- Ordered Counterfactual Explanation by Mixed-Integer Linear Optimization
- Interpretability Through Invertibility: A Deep Convolutional Network With Ideal Counterfactuals And Isosurfaces
- Counterfactual Generative Networks
- Learning "What-if" Explanations for Sequential Decision-Making
- GRACE: Generating Concise and Informative Contrastive Sample to Explain Neural Network Model’s Prediction
- An ASP-Based Approach to Counterfactual Explanations for Classification
- DECE: Decision Explorer with Counterfactual Explanations for Machine Learning Models
- Good Counterfactuals and Where to Find Them: A Case-Based Technique for Generating Counterfactuals for Explainable AI (XAI)
- Learning Global Transparent Models from Local Contrastive Explanations
- Decisions, Counterfactual Explanations and Strategic Behavior
- Algorithmic recourse under imperfect causal knowledge a probabilistic approach
- Beyond Individualized Recourse: Interpretable and Interactive Summaries of Actionable Recourses
- Learning What Makes a Difference from Counterfactual Examples and Gradient Supervision
- Counterfactual Vision-and-Language Navigation via Adversarial Path Sampler
- CERTIFAI: Counterfactual Explanations for Robustness, Transparency, Interpretability, and Fairness of Artificial Intelligence models
- FACE: Feasible and Actionable Counterfactual Explanation
- Counterfactual Explanations & Adversarial Examples
- Instance-Based Counterfactual Explanations for Time Series Classification
- On Relating 'Why?' and 'Why Not?' Explanations
- Model extraction from counterfactual explanations
- Efficient computation of counterfactual explanations of LVQ models
- Plausible Counterfactuals: Auditing Deep Learning Classifiers with Realistic Adversarial Examples
- ViCE: Visual Counterfactual Explanations for Machine Learning Models
- Model extraction from counterfactual explanations
- On the Fairness of Causal Algorithmic Recourse
- Scaling Guarantees for Nearest Counterfactual Explanations
- PermuteAttack: Counterfactual Explanation of Machine Learning Credit Scorecards
- Decisions, Counterfactual Explanations and Strategic Behavior
- FOCUS: Flexible Optimizable Counterfactual Explanations for Tree Ensembles
- Interpretable and Interactive Summaries of Actionable Recourses
- Counterfactual Explanation Based on Gradual Construction for Deep Networks
- CounteRGAN: Generating Realistic Counterfactuals with Residual Generative Adversarial Nets
- EXPLAINABLE IMAGE CLASSIFICATION WITH EVIDENCE COUNTERFACTUAL
- Model-Agnostic Counterfactual Explanations for Consequential Decisions
- Explaining Data-Driven Decisions made by AI Systems: The Counterfactual Approach
- On the Fairness of Causal Algorithmic Recourse
- On Counterfactual Explanations under Predictive Multiplicity
- EXPLANATION BY PROGRESSIVE EXAGGERATION
- Algorithmic Recourse: from Counterfactual Explanations to Interventions
- Learning Model-Agnostic Counterfactual Explanations for Tabular Data - chvae)
- The hidden assumptions behind counterfactual explanations and principal reasons
- Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations
- The philosophical basis of algorithmic recourse
- Why Does My Model Fail? Contrastive Local Explanations for Retail Forecasting
- Convex Density Constraints for Computing Plausible Counterfactual Explanations
- Fast Real-time Counterfactual Explanations
- CRUDS: Counterfactual Recourse Using Disentangled Subspaces
- DACE: Distribution-Aware Counterfactual Explanation by Mixed-Integer Linear Optimization
- Relation-Based Counterfactual Explanations for Bayesian Network Classifiers
- PRINCE: Provider-side Interpretability with Counterfactual Explanations in Recommender Systems
- CoCoX: Generating Conceptual and Counterfactual Explanations via Fault-Lines
- SCOUT: Self-aware Discriminant Counterfactual Explanations
- Multi-Objective Counterfactual Explanations
- EMAP: Explanation by Minimal Adversarial Perturbation
- Random forest explainability using counterfactual sets
- The Dangers of Post-hoc Interpretability: Unjustified Counterfactual Explanations
- Multimodal Explanations by Predicting Counterfactuality in Videos
- Unjustified Classification Regions and Counterfactual Explanations In Machine Learning - PDKK 2019
- Factual and Counterfactual Explanations for Black Box Decision Making
- Model Agnostic Contrastive Explanations for Structured Data
- Counterfactuals uncover the modular structure of deep generative models
- Actionable Interpretability through Optimizable Counterfactual Explanations for Tree Ensembles
- Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems
- Generative Counterfactual Introspection for Explainable Deep Learning
- Generating Counterfactual and Contrastive Explanations using SHAP
- Interpretable Counterfactual Explanations Guided by Prototypes
- Counterfactual Visual Explanations
- Counterfactual Explanation Algorithms for Behavioral and Textual Data
- Synthesizing Action Sequences for Modifying Model Decisions
- Measurable Counterfactual Local Explanations for Any Classifier
- Generating Contrastive Explanations with Monotonic Attribute Functions
- The What-If Tool: Interactive Probing of Machine Learning Models
- Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers
- Actionable Recourse in Linear Classification
- EQUALIZING RECOURSE ACROSS GROUPS
- Efficient Search for Diverse Coherent Explanations
- Counterfactual explanations of machine learning predictions: opportunities and challenges for AI safety
- Explaining image classifiers by counterfactual generation
- Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR
- Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives - Explanation-Method)
- Interpreting Neural Network Judgments via Minimal, Stable, and Symbolic Corrections
- Interpretable Credit Application Predictions With Counterfactual Explanations
- Comparison-based Inverse Classification for Interpretability in Machine Learning
- Generating Counterfactual Explanations with Natural Language
- Grounding Visual Explanations
- Interpreting Neural Network Judgments via Minimal, Stable, and Symbolic Corrections
- Inverse Classification for Comparison-based Interpretability in Machine Learning
- Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking
- When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness
- A budget-constrained inverse classification framework for smooth classifiers
- Generalized Inverse Classification
- Explaining Data-Driven Document Classifications
- The Inverse Classification Problem
- The cost-minimizing inverse classification problem: a genetic algorithm approach
- https://github.com/SeldonIO/alibi
- https://github.com/ustunb/actionable-recourse - Learn 
- https://github.com/andreArtelt/ceml - Learn 
- https://github.com/interpretml/DiCE.git
- https://github.com/MarcelRobeer/ContrastiveExplanation - learn, 
- https://github.com/divyat09/cf-feasibility - Learn, 
- https://github.com/amirhk/mace - Learn, 
- https://github.com/Networks-Learning/strategic-decisions - learn 
- https://github.com/IBM/Contrastive-Explanation-Method - Explanation-Method?style=social)
Programming Languages
Keywords
machine-learning
5
counterfactual-explanations
4
explainable-ai
4
xai
3
explainable-ml
3
interpretability
2
counterfactual
1
explanations
1
python
1
contrastive-explanations
1
foil
1
model-agnostic
1
causality
1
neurips-2019
1
interpretable-machine-learning
1
algorithmic-recourse
1
decision-making
1
incentives
1
strategic-ml
1
ibm-research-ai
1