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https://github.com/wangyongjie-ntu/awesome-counterfactual-explanations

awesome counterfactual explanations
https://github.com/wangyongjie-ntu/awesome-counterfactual-explanations

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awesome counterfactual explanations

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# Counterfactual Explanation (Actionable Recourse)



Counterfactual explanations mainly target to find the mimimum perturbation which changes the original prediction(Ususlly from an undesirable prediction to ideal one). The perturbation itself is a valid instance following the real data distribution as the training samples. It has broad applications, E.g., finance, education, health care ect. Specifically, what should I do to get the credit card approved if I received the rejection. This task can be viewed as extracting knowledge/solutions from the black-box models. It belongs to the instance-level explanation. Quite interesting to dive deeper!!!



The two use cases of counterfactual explanations:



![counterfactual explanations](https://github.com/iversonicter/awesome-explainable-ai/blob/master/fig/cf.png)



## Survey papers 



[Counterfactuals and Causability in Explainable Artificial Intelligence: Theory, Algorithms, and Applications](https://arxiv.org/abs/2103.04244), Arxiv preprint 2021



[A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9321372), IEEE Access 2021



[Good Counterfactuals and Where to Find Them: A Case-Based Technique for Generating Counterfactuals for Explainable AI (XAI)](https://arxiv.org/pdf/2005.13997.pdf), Arxiv preprint 2020



[Counterfactual Explanations for Machine Learning: A Review](https://arxiv.org/pdf/2010.10596.pdf), Arxiv preprint 2020



[A survey of algorithmic recourse: definitions, formulations, solutions, and prospects](https://arxiv.org/abs/2010.04050), Arxiv preprint 2020



[On the computation of counterfactual explanations -- A survey](https://arxiv.org/abs/1911.07749), Arxiv preprint 2019



[Issues with post-hoc counterfactual explanations: a discussion](https://arxiv.org/abs/1906.04774), ICML Workshop 2019



[Counterfactuals in Explainable Artificial Intelligence (XAI): Evidence from Human Reasoning](https://www.ijcai.org/Proceedings/2019/0876.pdf), IJCAI 2019



## Papers



[A Few Good Counterfactuals: Generating Interpretable, Plausible & Diverse Counterfactual Explanations](https://arxiv.org/pdf/2101.09056.pdf), Arxiv preprint 2021



[GeCo: Quality Counterfactual Explanations in Real Time](https://arxiv.org/pdf/2101.01292.pdf), Arxiv preprint 2021



[Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties](https://arxiv.org/abs/2103.08951), AISTATS 2021, [code](https://github.com/oscarkey/explanations-by-minimizing-uncertainty)



[FIMAP: Feature Importance by Minimal Adversarial Perturbation](https://www.aaai.org/AAAI21Papers/AAAI-2721.Chapman-RoundsM.pdf), AAAI 2021



[Generate Your Counterfactuals: Towards Controlled Counterfactual Generation for Text](https://arxiv.org/pdf/2012.04698.pdf), AAAI 2021



[Counterfactual Fairness with Disentangled Causal Effect Variational Autoencoder](https://arxiv.org/pdf/2011.11878.pdf), AAAI 2021



[Counterfactual Explanations for Oblique Decision Trees: Exact, Efficient Algorithms](https://arxiv.org/abs/2103.01096), AAAI 2021



[Ordered Counterfactual Explanation by Mixed-Integer Linear Optimization](https://arxiv.org/abs/2012.11782), AAAI 2021



[Interpretability Through Invertibility: A Deep Convolutional Network With Ideal Counterfactuals And Isosurfaces](https://openreview.net/forum?id=8YFhXYe1Ps)



[Counterfactual Generative Networks](http://www.cvlibs.net/publications/Sauer2021ICLR.pdf), ICLR 2021, [code](https://github.com/autonomousvision/counterfactual_generative_networks)



[Learning "What-if" Explanations for Sequential Decision-Making](https://openreview.net/forum?id=h0de3QWtGG), ICLR 2021



[GRACE: Generating Concise and Informative Contrastive Sample to Explain Neural Network Model’s Prediction](https://arxiv.org/abs/1911.02042), KDD 2020 [code](https://github.com/lethaiq/GRACE_KDD20)



[An ASP-Based Approach to Counterfactual Explanations for Classification](https://arxiv.org/pdf/2004.13237.pdf), RuleML + PR 2020



[DECE: Decision Explorer with Counterfactual Explanations for Machine Learning Models](https://arxiv.org/pdf/2008.08353.pdf), TVCG 2020



[Good Counterfactuals and Where to Find Them: A Case-Based Technique for Generating Counterfactuals for Explainable AI (XAI)](https://arxiv.org/pdf/2005.13997.pdf), ICCBR 2020



[Learning Global Transparent Models from Local Contrastive Explanations](https://arxiv.org/abs/2002.08247), NeurIPS 2020



[Decisions, Counterfactual Explanations and Strategic Behavior](https://arxiv.org/abs/2002.04333), NeurIPS 2020



[Algorithmic recourse under imperfect causal knowledge a probabilistic approach](https://arxiv.org/abs/2006.06831), NeurIPS 2020



[Beyond Individualized Recourse: Interpretable and Interactive Summaries of Actionable Recourses](https://arxiv.org/abs/2009.07165), NeurIPS 2020



[Learning What Makes a Difference from Counterfactual Examples and Gradient Supervision](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123550579.pdf), ECCV 2020



[Counterfactual Vision-and-Language Navigation via Adversarial Path Sampler](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123510069.pdf), ECCV 2020



[CERTIFAI: Counterfactual Explanations for Robustness, Transparency, Interpretability, and Fairness of Artificial Intelligence models](https://arxiv.org/pdf/1905.07857.pdf),  AAAI/AIES 2020



[FACE: Feasible and Actionable Counterfactual Explanation](https://dl.acm.org/doi/abs/10.1145/3375627.3375850), AAAI/AIES 2020



[Counterfactual Explanations & Adversarial Examples](https://arxiv.org/pdf/2009.05487.pdf), Arxiv preprint 2020



[Instance-Based Counterfactual Explanations for Time Series Classification](https://arxiv.org/pdf/2009.13211.pdf), Arxiv preprint 2020



[On Relating 'Why?' and 'Why Not?' Explanations](https://arxiv.org/abs/2012.11067), Arxiv preprint 2020



[Model extraction from counterfactual explanations](https://arxiv.org/pdf/2009.01884.pdf), Arxiv preprint 2020



[Efficient computation of counterfactual explanations of LVQ models](https://arxiv.org/pdf/1908.00735.pdf), Arxiv preprint 2020



[Plausible Counterfactuals: Auditing Deep Learning Classifiers with Realistic Adversarial Examples](https://arxiv.org/pdf/2003.11323.pdf), Arxiv preprint 2020



[ViCE: Visual Counterfactual Explanations for Machine Learning Models](https://arxiv.org/pdf/2003.02428.pdf), IUI 2020



[Model extraction from counterfactual explanations](https://arxiv.org/pdf/2009.01884.pdf), Arxiv 2020



[On the Fairness of Causal Algorithmic Recourse](https://arxiv.org/pdf/2010.06529.pdf), Arxiv preprint 2020



[Scaling Guarantees for Nearest Counterfactual Explanations](https://arxiv.org/abs/2010.04965), Arxiv preprint 2020



[PermuteAttack: Counterfactual Explanation of Machine Learning Credit Scorecards](https://arxiv.org/pdf/2008.10138.pdf), Arxiv preprint 2020



[Decisions, Counterfactual Explanations and Strategic Behavior](https://arxiv.org/abs/2002.04333), Arxiv preprint 2020



[FOCUS: Flexible Optimizable Counterfactual Explanations for Tree Ensembles](https://arxiv.org/abs/1911.12199), Arxiv preprint 2020



[Interpretable and Interactive Summaries of Actionable Recourses](https://arxiv.org/abs/2009.07165), Arxiv preprint 2020



[Counterfactual Explanation Based on Gradual Construction for Deep Networks](https://arxiv.org/abs/2008.01897), Arxiv preprint 2020



[CounteRGAN: Generating Realistic Counterfactuals with Residual Generative Adversarial Nets](https://arxiv.org/pdf/2009.05199.pdf), Arxiv preprint 2020



[EXPLAINABLE IMAGE CLASSIFICATION WITH EVIDENCE COUNTERFACTUAL](https://arxiv.org/pdf/2004.07511.pdf), Arxiv preprint 2020



[Model-Agnostic Counterfactual Explanations for Consequential Decisions](https://arxiv.org/abs/1905.11190), AISTATS 2020



[Explaining Data-Driven Decisions made by AI Systems: The Counterfactual Approach](https://arxiv.org/pdf/2001.07417.pdf), Arxiv preprint 2020



[On the Fairness of Causal Algorithmic Recourse](https://arxiv.org/pdf/2010.06529.pdf), Arxiv preprint 2020



[On Counterfactual Explanations under Predictive Multiplicity](http://proceedings.mlr.press/v124/pawelczyk20a.html), UAI 2020



[EXPLANATION BY PROGRESSIVE EXAGGERATION](https://iclr.cc/virtual_2020/poster_H1xFWgrFPS.html), ICLR 2020



[Algorithmic Recourse: from Counterfactual Explanations to Interventions](https://arxiv.org/abs/2002.06278), Arxiv preprint 2020



[Learning Model-Agnostic Counterfactual Explanations for Tabular Data](https://dl.acm.org/doi/abs/10.1145/3366423.3380087),  ACM WWW 2020, [code](https://github.com/MartinPawel/c-chvae)



[The hidden assumptions behind counterfactual explanations and principal reasons](https://arxiv.org/pdf/1912.04930.pdf), ACM Facct 2020



[Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations](https://arxiv.org/pdf/1905.07697.pdf), ACM Facct 2020



[The philosophical basis of algorithmic recourse](https://dl.acm.org/doi/abs/10.1145/3351095.3372876), ACM Facct 2020



[Why Does My Model Fail? Contrastive Local Explanations for Retail Forecasting](https://arxiv.org/pdf/1908.00085.pdf), ACM Facct 2020



[Convex Density Constraints for Computing Plausible Counterfactual Explanations](https://arxiv.org/pdf/2002.04862.pdf), ICANN 2020



[Fast Real-time Counterfactual Explanations](https://arxiv.org/pdf/2007.05684.pdf), ICML 2020 Workshop



[CRUDS: Counterfactual Recourse Using Disentangled Subspaces](https://finale.seas.harvard.edu/files/finale/files/cruds-_counterfactual_recourse_using_disentangled_subspaces.pdf), ICML 2020 Workshop



[DACE: Distribution-Aware Counterfactual Explanation by Mixed-Integer Linear Optimization](https://www.ijcai.org/Proceedings/2020/0395.pdf), IJCAI 2020



[Relation-Based Counterfactual Explanations for Bayesian Network Classifiers](https://www.ijcai.org/Proceedings/2020/63), IJCAI 2020



[PRINCE: Provider-side Interpretability with Counterfactual Explanations in Recommender Systems](https://arxiv.org/pdf/1911.08378.pdf), WSDM 2020



[CoCoX: Generating Conceptual and Counterfactual Explanations via Fault-Lines](https://aaai.org/ojs/index.php/AAAI/article/view/5643/5499), AAAI 2020



[SCOUT: Self-aware Discriminant Counterfactual Explanations](https://openaccess.thecvf.com/content_CVPR_2020/papers/Wang_SCOUT_Self-Aware_Discriminant_Counterfactual_Explanations_CVPR_2020_paper.pdf), CVPR 2020, [code](https://github.com/peiwang062/SCOUT)



[Multi-Objective Counterfactual Explanations](https://arxiv.org/pdf/2004.11165.pdf), PPSN 2020



[EMAP: Explanation by Minimal Adversarial Perturbation](https://arxiv.org/pdf/1912.00872.pdf), AAAI 2020



[Random forest explainability using counterfactual sets](https://doi.org/10.1016/j.inffus.2020.07.001), Information Fusion 2020



[The Dangers of Post-hoc Interpretability: Unjustified Counterfactual Explanations](https://www.ijcai.org/proceedings/2019/388), IJCAI 2019



[Multimodal Explanations by Predicting Counterfactuality in Videos](https://arxiv.org/pdf/1812.01263.pdf), CVPR 2019



[Unjustified Classification Regions and Counterfactual Explanations In Machine Learning](https://ecmlpkdd2019.org/downloads/paper/226.pdf), ECML-PDKK 2019



[Factual and Counterfactual Explanations for Black Box Decision Making](https://ieeexplore.ieee.org/document/8920138), IEEE Intelligent Systems 2019



[Model Agnostic Contrastive Explanations for Structured Data](https://arxiv.org/pdf/1906.00117.pdf), Arxiv preprint 2019



[Counterfactuals uncover the modular structure of deep generative models](https://arxiv.org/pdf/1812.03253.pdf), Arxiv preprint 2019



[Actionable Interpretability through Optimizable Counterfactual Explanations for Tree Ensembles](https://staff.fnwi.uva.nl/m.derijke/wp-content/papercite-data/pdf/lucic-2019-actionable-arxiv.pdf), arxiv preprint 2019



[Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems](https://arxiv.org/pdf/1907.09615.pdf), Arxiv preprint 2019



[Generative Counterfactual Introspection for Explainable Deep Learning](https://arxiv.org/abs/1907.03077), Arxiv preprint 2019



[Generating Counterfactual and Contrastive Explanations using SHAP](https://arxiv.org/pdf/1906.09293.pdf), Arxiv preprint 2019



[Interpretable Counterfactual Explanations Guided by Prototypes](https://arxiv.org/pdf/1907.02584.pdf), Arxiv preprint 2019



[Counterfactual Visual Explanations](https://arxiv.org/pdf/1904.07451.pdf), ICML 2019



[Counterfactual Explanation Algorithms for Behavioral and Textual Data](https://arxiv.org/abs/1912.01819), Arxiv preprint 2019



[Synthesizing Action Sequences for Modifying Model Decisions](https://arxiv.org/pdf/1910.00057.pdf), Arxiv preprint 2019



[Measurable Counterfactual Local Explanations for Any Classifier](https://arxiv.org/pdf/1908.03020.pdf), Arxiv preprint 2019



[Generating Contrastive Explanations with Monotonic Attribute Functions](https://arxiv.org/pdf/1905.12698.pdf), arxiv preprint 2019



[The What-If Tool: Interactive Probing of Machine Learning Models](https://arxiv.org/pdf/1907.04135v2.pdf), TVCG 2019



[Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers](https://arxiv.org/abs/1912.03277), NeurIPS Workshop 2019



[Actionable Recourse in Linear Classification](https://dl.acm.org/doi/pdf/10.1145/3287560.3287566), ACM Facct 2019



[EQUALIZING RECOURSE ACROSS GROUPS](https://arxiv.org/abs/1909.03166),  Arxiv preprint 2019



[Efficient Search for Diverse Coherent Explanations](https://arxiv.org/pdf/1901.04909.pdf), ACM Facct 2019



[Counterfactual explanations of machine learning predictions: opportunities and challenges for AI safety](http://ceur-ws.org/Vol-2301/paper_20.pdf), SafeAI@AAAI 2019



[Explaining image classifiers by counterfactual generation](https://arxiv.org/pdf/1807.08024.pdf), ICLR 2019



[Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR](https://arxiv.org/abs/1711.00399), Hardvard Journal of Law & Technology 2018 (strong recommend)



[Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives](https://papers.nips.cc/paper/7340-explanations-based-on-the-missing-towards-contrastive-explanations-with-pertinent-negatives), NIPS 2018, [code](https://github.com/IBM/Contrastive-Explanation-Method)



[Interpreting Neural Network Judgments via Minimal, Stable, and Symbolic Corrections](https://arxiv.org/pdf/1802.07384.pdf), NIPS 2018



[Interpretable Credit Application Predictions With Counterfactual Explanations](https://arxiv.org/abs/1811.05245), Arxiv preprint 2018



[Comparison-based Inverse Classification for Interpretability in Machine Learning](https://hal.sorbonne-universite.fr/hal-01905982/document), IPMU 2018



[Generating Counterfactual Explanations with Natural Language](https://arxiv.org/pdf/1806.09809.pdf), ICML 2018 Workshop



[Grounding Visual Explanations](https://arxiv.org/pdf/1807.09685.pdf), ECCV 2018



[Interpreting Neural Network Judgments via Minimal, Stable, and Symbolic Corrections](https://proceedings.neurips.cc/paper/2018/file/300891a62162b960cf02ce3827bb363c-Paper.pdf), NeurIPS 2018



[Inverse Classification for Comparison-based Interpretability in Machine Learning](https://arxiv.org/pdf/1712.08443.pdf), Arxiv 2017



[Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking](https://arxiv.org/pdf/1706.06691.pdf), KDD 2017



[When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness](https://proceedings.neurips.cc/paper/2017/file/1271a7029c9df08643b631b02cf9e116-Paper.pdf), NeurIPS 2017



[A budget-constrained inverse classification framework for smooth classifiers](https://arxiv.org/abs/1605.09068), ICDMW 2017



[Generalized Inverse Classification](https://epubs.siam.org/doi/pdf/10.1137/1.9781611974973.19), SIAM 2017



[Explaining Data-Driven Document Classifications](http://pages.stern.nyu.edu/~fprovost/Papers/MartensProvost_Explaining.pdf), MIS Quarterly 2014



[The Inverse Classification Problem](https://link.springer.com/article/10.1007/s11390-010-9337-x), Journal of Computer Science and Technology 2010



[The cost-minimizing inverse classification problem: a genetic algorithm approach](https://www.sciencedirect.com.remotexs.ntu.edu.sg/science/article/pii/S0167923600000774), Decision Support Systems 2000



## Github Repos



Alibi: [https://github.com/SeldonIO/alibi](https://github.com/SeldonIO/alibi) ![](https://img.shields.io/github/stars/SeldonIO/alibi.svg?style=social)



actionable-recourse: [https://github.com/ustunb/actionable-recourse](https://github.com/ustunb/actionable-recourse), Scikit-Learn ![](https://img.shields.io/github/stars/ustunb/actionable-recourse?style=social)



CEML: [https://github.com/andreArtelt/ceml](https://github.com/andreArtelt/ceml), Pytorch, Keras, Tensorflow, Scikit-Learn ![](https://img.shields.io/github/stars/andreArtelt/ceml?style=social)



Dice: [https://github.com/interpretml/DiCE.git](https://github.com/interpretml/DiCE.git), Pytorch, TensorFlow ![](https://img.shields.io/github/stars/interpretml/DiCE?style=social)



ContrastiveExplanation: [https://github.com/MarcelRobeer/ContrastiveExplanation](https://github.com/MarcelRobeer/ContrastiveExplanation), scikit-learn, ![](https://img.shields.io/github/stars/MarcelRobeer/ContrastiveExplanation?style=social)



cf-feasibility: [https://github.com/divyat09/cf-feasibility](https://github.com/divyat09/cf-feasibility), Pytorch, Tensorflow, Scikit-Learn, ![](https://img.shields.io/github/stars/divyat09/cf-feasibility?style=social)



Mace: [https://github.com/amirhk/mace](https://github.com/amirhk/mace), Scikit-Learn, ![](https://img.shields.io/github/stars/amirhk/mace?style=social)



Strategic-Decisions: [https://github.com/Networks-Learning/strategic-decisions](https://github.com/Networks-Learning/strategic-decisions), Scikit-learn ![](https://img.shields.io/github/stars/Networks-Learning/strategic-decisions?style=social)



Contrastive-Explanation-Method: [https://github.com/IBM/Contrastive-Explanation-Method](https://github.com/IBM/Contrastive-Explanation-Method), Tensorflow ![](https://img.shields.io/github/stars/IBM/Contrastive-Explanation-Method?style=social)