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https://github.com/fulifeng/Causal_Reading_Group

We will keep updating the paper list about machine learning + causal theory. We also internally discuss related papers between NExT++ (NUS) and LDS (USTC) by week.
https://github.com/fulifeng/Causal_Reading_Group

causal-inference causality deep-learning machine-learning recommender-system

Last synced: about 1 month ago
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We will keep updating the paper list about machine learning + causal theory. We also internally discuss related papers between NExT++ (NUS) and LDS (USTC) by week.

Awesome Lists containing this project

README 

        
# Causal_Reading_Group

This is a list of papers about causality.



## Table of Contents

- [Survey paper](#survey-paper)

- [Dataset](#dataset)

- [Foundamental Causality](#foundamental-causality)

- [Causality in Machine Learning](#causality-in-machine-learning)

- [Causal Recommendation](#causal-recommendation)

- [Causal Computer Vision](#causal-computer-vision)

- [Causality in NLP](#causality-in-nlp)

- [Causal Interpretability](#causal-interpretability)



## Survey Paper

- [Causal Machine Learning: A Survey and Open Problems] (https://arxiv.org/abs/2206.15475)(2022)

- [D'ya like DAGs? A Survey on Structure Learning and Causal Discovery]() (2021)

- [Causal Interpretability for Machine Learning -- Problems, Methods and Evaluation](https://arxiv.org/abs/2003.03934) (2020KDD)

- [A Survey of Learning Causality with Data: Problems and Methods](https://arxiv.org/abs/1809.09337) (2020)

- [A Survey on Causal Inference](https://arxiv.org/abs/2002.02770) (2020)



## Dataset

- [ACIC 2018 Data Challenge](https://www.cmu.edu/acic2018/data-challenge/index.html) (2018ACIC)



## Foundamental Causality

- [Estimating Identifiable Causal Effects on Markov Equivalence Class through Double Machine Learning]() (2021ICML)

- [A Proxy Variable View of Shared Confounding]() (2021ICML)

- [Valid Causal Inference with (Some) Invalid Instruments]() (2021ICML)

- [Integer Programming for Causal Structure Learning in the Presence of Latent Variables]() (2021ICML)

- [Operationalizing Complex Causes: A Pragmatic View of Mediation]() (2021ICML)

- [Permutation Weighting]() (2021ICML)

- [Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding]() (2021ICML)

- [How and Why to Use Experimental Data to Evaluate Methods for Observational Causal Inference]() (2021ICML)

- [Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction]() (2021ICML)

- [Model-Free and Model-Based Policy Evaluation when Causality is Uncertain]() (2021ICML)

- [Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning](https://arxiv.org/abs/1706.03461) (2019PNAS)

- [Unit Selection Based on Counterfactual Logic](https://ftp.cs.ucla.edu/pub/stat_ser/r488.pdf) (2019IJCAI)

- [Counterfactual regression with importance sampling weights](https://www.ijcai.org/Proceedings/2019/0815.pdf) (2019IJCAI)

- [Orthogonal Random Forest for Causal Inference](https://arxiv.org/abs/1806.03467) (2019ICML)

- [Estimation and Inference of Heterogeneous Treatment Effects using Random Forests](https://arxiv.org/abs/1510.04342) (2018JASA)

- [Estimating individual treatment effect: generalization bounds and algorithms](https://arxiv.org/abs/1606.03976) (2017JMLR)

- [Towards a learning theory of cause-effect inference](http://proceedings.mlr.press/v37/lopez-paz15.html) (2015ICML)



## Causality in Machine Learning

- [Causal Reasoning in Simulation for Structure and Transfer Learning of Robot Manipulation Policies](https://arxiv.org/abs/2103.16772) (2021ICRA)

- [Generative Causal Explanations for Graph Neural Networks](https://arxiv.org/pdf/2104.06643.pdf)(2021ICML)

- [Regularizing towards Causal Invariance: Linear Models with Proxies]() (2021ICML)

- [Causality-aware counterfactual confounding adjustment as an alternative to linear residualization in anticausal prediction tasks based on linear learners]() (2021ICML)

- [On Disentangled Representations Learned from Correlated Data]() (2021ICML)

- [Modularity in Reinforcement Learning via Algorithmic Independence in Credit Assignment] (2021ICML)

- [Domain Generalization using Causal Matching]() (2021ICML)

- [Selecting Data Augmentation for Simulating Interventions]() (2021ICML)

- [Necessary and sufficient conditions for causal feature selection in time series with latent common causes]() (2021ICML)

- [Out-of-Distribution Generalization via Risk Extrapolation (REx)]() (2021ICML)

- [Counterfactual Credit Assignment in Model-Free Reinforcement Learning]() (2021ICML)

- [Size-Invariant Graph Representations for Graph Classification Extrapolations]() (2021ICML)

- [Adapting Interactional Observation Embedding for Counterfactual Learning to Rank]() (2021SIGIR)

- [Learning Individually Fair Classifier with Path-Specific Causal-Effect Constraint](http://proceedings.mlr.press/v130/chikahara21a.html)(2021AISTATS)

- [Path-specific Counterfactual Fairness](https://ojs.aaai.org//index.php/AAAI/article/view/4777) (2019AAAI)

- [Counterfactual Fairness](https://proceedings.neurips.cc/paper/2017/file/a486cd07e4ac3d270571622f4f316ec5-Paper.pdf) (2017 NIPS)

- [A Causal View on Robustness of Neural Networks](https://arxiv.org/abs/2005.01095) (2020NeurIPS)

- [An investigation of why overparameterization exacerbates spurious correlations](https://arxiv.org/pdf/2005.04345.pdf) (2020)

- [Matching in Selective and Balanced Representation Space for Treatment Effects Estimation](https://dl.acm.org/doi/abs/10.1145/3340531.3412037) (2020CIKM)

- [Improving the accuracy of medical diagnosis with causal machine learning](https://www.nature.com/articles/s41467-020-17419-7) (2020Nature Communication)

- [Causal Meta-Mediation Analysis: Inferring Dose-Response Function From Summary Statistics of Many Randomized Experiments](https://dl.acm.org/doi/abs/10.1145/3394486.3403313) (2020KDD)

- [Adapting Text Embeddings for Causal Inference](http://proceedings.mlr.press/v124/veitch20a.html) (2020UAI)

- [Double/Debiased/Neyman Machine Learning of Treatment Effects](https://arxiv.org/abs/1701.08687) (2017American Economic Review)

- [Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects](https://arxiv.org/pdf/1706.09523.pdf)(2020)

- [Deep IV: A Flexible Approach for Counterfactual Prediction](http://proceedings.mlr.press/v70/hartford17a/hartford17a.pdf)(2017)

- [Estimating individual treatment effect: generalization bounds and algorithms](https://arxiv.org/pdf/1606.03976.pdf)(2017)

- [Causal Decision Trees](https://arxiv.org/pdf/1508.03812.pdf)(2020)

- [Estimating Individual Treatment Effect in Observational Data Using Random Forest Methods](https://arxiv.org/pdf/1701.05306.pdf)(2018)



## Causal Recommendation

- [CausalInt: Causal Inspired Intervention for Multi-Domain Recommendation](2022KDD)

- [Practical Counterfactual Policy Learning for Top-𝐾 Recommendations](https://www.csie.ntu.edu.tw/~cjlin/papers/counterfactual_topk/xcf.pdf)(2022KDD,IPS) 

- [CausalInt: Causal Inspired Intervention for Multi-Domain Recommendation](https://assets.amazon.science/9a/f0/d567c24f4b7080def22ccd09cb58/aspire-air-shipping-recommendation-for-e-commerce-products-via-causal-inference-framework.pdf)(2022KDD,DR+uplift)

- [On the Opportunity of Causal Learning in Recommendation Systems: Foundation, Estimation, Prediction and Challenges](https://www.ijcai.org/proceedings/2022/0787.pdf)(2022IJCAI, causal analysis framework)

- [Deconfounding Duration Bias in Watch-time Prediction for Video Recommendation.](https://arxiv.org/abs/2206.06003)(2022KDD,backdoor for duration bias)  

- [Addressing Unmeasured Confounder for Recommendation with Sensitivity Analysis](http://staff.ustc.edu.cn/~hexn/papers/kdd22-deconfound-rec.pdf)(2022KDD,Adversarial IPS --> unmeasured confounder)

- [Mitigating Hidden Confounding Effects for Causal Recommendation](https://arxiv.org/abs/2205.07499)(2022arxive,frontdoor)

- [Addressing Confounding Feature Issue for Causal Recommendation](https://arxiv.org/abs/2205.06532) (2022 TOIS,casual effect + pathe-specific causal effect)

- [Tutorial-Causal Recommendation: Progresses and Future Directions](https://causalrec.github.io/) (2022 WWW tutorial) 

- [User-controllable Recommendation Against Filter Bubbles](https://arxiv.org/pdf/2204.13844.pdf)(2022 SIGIR, counterfactual)

- [ESCM^2: Entire Space Counterfactual Multi-Task Model for Post-Click Conversion Rate Estimation](https://arxiv.org/abs/2204.05125) (2022 SIGIR, IPW+multitask)

- [Causal Disentanglement for Semantics-Aware Intent Learning in Recommendation](https://ieeexplore.ieee.org/document/9736612) (2022 TKDE, deconfounding)

- [Rating Distribution Calibration for Selection Bias Mitigation in Recommendations](https://dl.acm.org/doi/10.1145/3485447.3512078)(2022 www, selection bias)

- [Unbiased Sequential Recommendation with Latent Confounders](https://dl.acm.org/doi/10.1145/3485447.3512092) (2022 WWW, IPS for seqrec)

- [A Model-Agnostic Causal Learning Framework for Recommendation using Search Data](https://arxiv.org/pdf/2202.04514.pdf) (2022 WWW, instrumental variable)

- [CausPref: Causal Preference Learning for Out-of-Distribution Recommendation](https://arxiv.org/pdf/2202.03984.pdf) (2022 WWW)

- [Estimating Causal Effects of Multi-Aspect Online Reviews with Multi-Modal Proxies](https://arxiv.org/pdf/2112.10274.pdf) (2022 WSDM)

- [Towards Unbiased and Robust Causal Ranking for Recommender Systems](https://dl.acm.org/doi/abs/10.1145/3488560.3498521) (2022 WSDM)

- [Causal Inference for Visual Debiasing in Visually-Aware Recommendation](https://dl.acm.org/doi/pdf/10.1145/3383313.3412241) (2021 ACM MM)

- [Mitigating Confounding Bias in Recommendation via Information Bottleneck](https://dl.acm.org/doi/10.1145/3460231.3474263) (2021 RecSys)

- [Online Evaluation Methods for the Causal Effect of Recommendations](https://dl.acm.org/doi/fullHtml/10.1145/3460231.3474235) (2021 RecSys)

- [Counterfactual Explainable Recommendation](https://arxiv.org/abs/2108.10539) (2021 CIKM) 

- [Counterfactual Review-based Recommendation](https://dl.acm.org/doi/abs/10.1145/3459637.3482244) (2021 CIKM)

- [Top-N Recommendation with Counterfactual User Preference Simulation](https://arxiv.org/abs/2109.02444) (2021 CIKM)

- [Causally Attentive Collaborative Filtering](https://dl.acm.org/doi/abs/10.1145/3459637.3482070) (2021 CIKM)

- [CauSeR: Causal Session-based Recommendations for Handling Popularity Bias](https://dl.acm.org/doi/abs/10.1145/3459637.3482071) (2021 CIKM)

- [CausCF: Causal Collaborative Filtering for Recommendation Effect Estimation](https://arxiv.org/pdf/2105.13881v1.pdf) (2021 CIKM)

- [A Semi-Synthetic Dataset Generation Framework for Causal Inference in Recommender Systems](https://openreview.net/forum?id=OqPBtOcFJt-) (2021)

- [Recommending the Most Effective Interventions to Improve Employment for Job Seekers with Disability](https://dl.acm.org/doi/abs/10.1145/3447548.3467095) (2021 KDD)

- [Deconfounded Recommendation for Alleviating Bias Amplification](https://arxiv.org/abs/2105.10648) (2021 KDD)

- [Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System](https://arxiv.org/abs/2010.15363) (2021 KDD)

- [Causal Intervention for Leveraging Popularity Bias in Recommendation](https://arxiv.org/abs/2105.06067)(2021 SIGIR)

- [Clicks can be Cheating: Counterfactual Recommendation for Mitigating Clickbait Issue](https://arxiv.org/pdf/2009.09945.pdf) (2021SIGIR)

- [CauseRec: Counterfactual User Sequence Synthesis for Sequential Recommendation](https://dl.acm.org/doi/pdf/10.1145/3404835.3462908) (2021SIGIR)

- [Personalized Counterfactual Fairness in Recommendation](https://arxiv.org/pdf/2105.09829.pdf) (2021SIGIR)

- [Counterfactual Data-Augmented Sequential Recommendation](https://dl.acm.org/doi/10.1145/3404835.3462855) (2021 SIGIR)

- [Counterfactual Reward Modification for Streaming Recommendation with Delayed Feedback]() (2021SIGIR)

- [Counterfactual Explanations for Neural Recommenders](https://arxiv.org/abs/2105.05008) (2021SIGIR)

- [The Simpson’s Paradox in the Offline Evaluation of Recommendation Systems](https://arxiv.org/pdf/2104.08912.pdf)(2021)

- [PRINCE: Provider-side Interpretability with Counterfactual Explanations in Recommender Systems](https://arxiv.org/abs/1911.08378v4) (2020WSDM)

- [Counterfactual Prediction for Bundle Treatment](https://proceedings.neurips.cc/paper/2020/file/e430ad64df3de73e6be33bcb7f6d0dac-Paper.pdf) (2020NeurIPS)

- [Adversarial Counterfactual Learning and Evaluation for Recommender System](https://papers.nips.cc/paper/2020/file/9cd013fe250ebffc853b386569ab18c0-Paper.pdf) (2020NeurIPS)

- [Information Theoretic Counterfactual Learning from Missing-Not-At-Random Feedback](https://papers.nips.cc/paper/2020/file/13f3cf8c531952d72e5847c4183e6910-Paper.pdf) (2020NeurIPS)

- [Learning Stable Graphs from Multiple Environments with Selection Bias](https://dl.acm.org/doi/abs/10.1145/3394486.3403270) (2020KDD)

- [Causal Inference for Recommender Systems](https://dl.acm.org/doi/10.1145/3383313.3412225) (2020 RecSys)

- [Debiasing Item-to-Item Recommendations With Small Annotated Datasets](https://dl.acm.org/doi/10.1145/3383313.3412265) (2020 RecSys)

- [Deconfounding User Satisfaction Estimation from Response Rate Bias](https://dl.acm.org/doi/10.1145/3383313.3412208) (2020 RecSys)

- [Unbiased Learning for the Causal Effect of Recommendation](https://dl.acm.org/doi/10.1145/3383313.3412261) (2020 RecSys)

- [Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback](https://arxiv.org/pdf/1909.03601.pdf) (2020WSDM)

- [A General Framework for Counterfactual Learning-to-Rank](http://www.cs.cornell.edu/people/tj/publications/agarwal_etal_19b.pdf) (2019SIGIR)

- [Doubly Robust Joint Learning for Recommendation on Data Missing Not at Random](http://proceedings.mlr.press/v97/wang19n/wang19n.pdf) (2019ICML)

- [Causal Embeddings for Recommendation: An Extended Abstract](https://www.ijcai.org/Proceedings/2019/0870.pdf) (2019IJCAI)

- [Unbiased Learning to Rank with Unbiased Propensity Estimation](https://arxiv.org/pdf/1804.05938.pdf) (2018SIGIR)

- [Recommendations as Treatments: Debiasing Learning and Evaluation](https://arxiv.org/abs/1602.05352) (2016ICML)

- [Estimating the Causal Impact of Recommendation Systems from Observational Data](https://arxiv.org/abs/1510.04342) (2015ACMEC)

- [Evaluating Online Ad Campaigns in a Pipeline: Causal Models At Scale](https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/36552.pdf) (2010KDD)



## Causal Computer Vision

- [Interventional Video Grounding with Dual Contrastive Learning](https://arxiv.org/pdf/2106.11013.pdf) (2021CVPR)

- [Deconfounded Video Moment Retrieval with Causal Intervention](https://arxiv.org/pdf/2106.01534.pdf) (2021SIGIR)

- [Causal Attention for Vision-Language Tasks](https://arxiv.org/abs/2103.03493) (2021CVPR)

- [Deconfounded Image Captioning: A Causal Retrospect](https://arxiv.org/abs/2003.03923) 

- [Counterfactual VQA: A Cause-Effect Look at Language Bias](https://arxiv.org/abs/2006.04315) (2021CVPR)

- [Visual Commonsense R-CNN](https://arxiv.org/abs/2002.12204) (2020CVPR)

- [More Grounded Image Captioning by Distilling Image-Text Matching Model](https://arxiv.org/abs/2004.00390) (2020CVPR)

- [Visual Commonsense Representation Learning via Causal Inference](https://openaccess.thecvf.com/content_CVPRW_2020/html/w26/Wang_Visual_Commonsense_Representation_Learning_via_Causal_Inference_CVPRW_2020_paper.html) (2020CVPR)

- [Counterfactual Samples Synthesizing for Robust Visual Question Answering](https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Counterfactual_Samples_Synthesizing_for_Robust_Visual_Question_Answering_CVPR_2020_paper.html) (2020CVPR)

- [Unbiased Scene Graph Generation From Biased Training](https://openaccess.thecvf.com/content_CVPR_2020/html/Tang_Unbiased_Scene_Graph_Generation_From_Biased_Training_CVPR_2020_paper.html) (2020CVPR)

- [Two Causal Principles for Improving Visual Dialog](https://openaccess.thecvf.com/content_CVPR_2020/html/Qi_Two_Causal_Principles_for_Improving_Visual_Dialog_CVPR_2020_paper.html) (2020CVPR)

- [Counterfactual Vision-and-Language Navigation via Adversarial Path Sampling](https://arxiv.org/abs/1911.07308) (2020ECCV)



## Causality in NLP

- [Uncovering Main Causalities for Long-tailed Information Extraction](https://arxiv.org/pdf/2109.05213v1.pdf) (2021EMNLP)

- [Empowering Language Understanding with Counterfactual Reasoning](https://arxiv.org/pdf/2106.03046.pdf) (2021ACL)

- [Everything Has a Cause: Leveraging Causal Inference in Legal Text Analysis](https://arxiv.org/abs/2104.09420) (2021NAACL)

- [How to make causal inferences using texts](https://arxiv.org/pdf/1802.02163.pdf) (2018arxiv)

- [Text and Causal Inference:A Review of Using Text to Remove Confounding from Causal Estimates](https://www.aclweb.org/anthology/2020.acl-main.474.pdf) (ACL2020)

- [Causal inference of script knowledge](https://www.aclweb.org/anthology/2020.emnlp-main.612.pdf) (2020EMNLP)

- [De-Biased Court’s View Generation with Causality](https://www.aclweb.org/anthology/2020.emnlp-main.56.pdf) (2020EMNLP)

- [Counterfactual Generator: A Weakly-Supervised Method for Named Entity Recognition](https://github.com/xijiz/cfgen/blob/master/docs/cfgen.pdf) (2020EMNLP)

- [Counterfactual Off-Policy Training for Neural Dialogue Generation](https://arxiv.org/abs/2004.14507) (2020EMNLP)

- [Identifying Spurious Correlations for Robust Text Classification](https://arxiv.org/pdf/2010.02458.pdf) (2020EMNLP)

- [Feature Selection as Causal Inference: Experiments with Text Classification](https://www.aclweb.org/anthology/K17-1018/) (2017CoNLL)



## Causal Interpretability

### Causal Discovery

- [OrphicX: A Causality-Inspired Latent Variable Model for Interpreting Graph Neural Networks](https://arxiv.org/pdf/2203.15209.pdf)(2022CVPR)

- [CausalVAE: Disentangled Representation Learning via Neural Structural Causal Models](https://arxiv.org/pdf/2004.08697.pdf)(2021CVPR)

- [Disentangled Generative Causal Representation Learning](https://arxiv.org/pdf/2010.02637.pdf)

- [Causal Inference with Deep Causal Graphs](https://arxiv.org/abs/2006.08380)

- [Causal Discovery with Reinforcement Learning](https://arxiv.org/abs/1906.04477)(2020ICLR)

- [Causal Discovery from Incomplete Data: A Deep Learning Approach](https://arxiv.org/pdf/2001.05343.pdf)(2020AAAI)

- [A Graph Autoencoder Approach to Causal Structure Learning](https://arxiv.org/abs/1911.07420) (2019-NeurIPS)

- [CXPlain: Causal Explanations for Model Interpretation under Uncertainty](https://papers.nips.cc/paper/2019/file/3ab6be46e1d6b21d59a3c3a0b9d0f6ef-Paper.pdf) (2019-NeurIPS)

- [Neural Network Attributions: A Causal Perspective](https://arxiv.org/abs/1902.02302) (2019-ICML)

- [Building Causal Graphs from Medical Literature and Electronic Medical Records](https://ojs.aaai.org//index.php/AAAI/article/view/3902)(2019-AAAI)

- [Explaining Deep Learning Models Using Causal Inference](https://arxiv.org/abs/1811.04376) (2018)

- [Neural Relational Inference for Interacting Systems](https://arxiv.org/pdf/1802.04687.pdf) (2018-ICML)

- [Learning Independent Causal Mechanisms](https://arxiv.org/abs/1712.00961) (2018-ICML)

- [DAGs with NO TEARS: Continuous Optimization for Structure Learning](https://papers.nips.cc/paper/2018/file/e347c51419ffb23ca3fd5050202f9c3d-Paper.pdf) (2018-NeurIPS)

- [A Causal Framework for Explaining the Predictions of Black-box Sequence-to-sequence Models](https://www.aclweb.org/anthology/D17-1042/) (2017-EMNLP)



### Causal Intervention

- [Causal Intervention for Weakly-Supervised Semantic Segmentation](https://arxiv.org/abs/2009.12547) (2020NeurIPS)

- [Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect](https://arxiv.org/abs/2009.12991) (2020NeurIPS)

- [Interventional Few-Shot Learning](https://arxiv.org/abs/2009.13000) (2020NeurIPS)

- [GAN Disssertion: Visualizing and Understnding Generative Adversarial Networks](https://arxiv.org/abs/1811.10597) (2018ICLR)

- [Causal Learning and Explanation of Deep Neural Networks via Autoencoded Activations](https://arxiv.org/abs/1802.00541) (2018)



### Counterfactual Interpretability

- [Model-Based Counterfactual Synthesizer for Interpretation](https://arxiv.org/pdf/2106.08971.pdf) (2021 KDD)

- [Counterfactual Explanations for Neural Recommenders](https://arxiv.org/abs/2105.05008) (2021SIGIR)

- [Algorithmic Recourse: from Counterfactual Explanations to Interventions](https://dl.acm.org/doi/abs/10.1145/3442188.3445899) (2021FAT)

- [CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks](https://arxiv.org/pdf/2102.03322.pdf) (2021)

- [Delta-CLUE Divere Sets of Explanations for Uncertainty Estimates](https://arxiv.org/abs/2104.06323) (2021ICLR workshop)

- [Explaining Deep Graph Networks with Molecular Counterfactuals](https://arxiv.org/abs/2011.05134) (2020NeurIPS)

- [Learning the Difference that Makes a Difference with Counterfactually-augmented Data](https://arxiv.org/abs/1909.12434) (2020ICLR)

- [Counterfactual Generator: A Weakly-Supervised Method for Named Entity Recognition](https://www.aclweb.org/anthology/2020.emnlp-main.590/) (2020EMNLP)

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

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

- [Explaining Image Classifiers by Counterfactual Generation](https://arxiv.org/abs/1807.08024) (2019ICLR)

- [Interpretable Counterfactual Explanations Guided by Prototypes](https://arxiv.org/abs/1907.02584) (2019)

- [Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations](https://arxiv.org/abs/1905.07697) (2019FAT)