{"id":13503573,"url":"https://github.com/fulifeng/Causal_Reading_Group","last_synced_at":"2025-03-29T18:31:20.909Z","repository":{"id":41257331,"uuid":"297018155","full_name":"fulifeng/Causal_Reading_Group","owner":"fulifeng","description":"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.","archived":false,"fork":false,"pushed_at":"2023-03-03T16:25:57.000Z","size":146,"stargazers_count":506,"open_issues_count":3,"forks_count":77,"subscribers_count":35,"default_branch":"master","last_synced_at":"2024-11-01T00:31:45.449Z","etag":null,"topics":["causal-inference","causality","deep-learning","machine-learning","recommender-system"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/fulifeng.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null}},"created_at":"2020-09-20T06:43:51.000Z","updated_at":"2024-10-22T09:51:52.000Z","dependencies_parsed_at":"2024-01-16T10:37:08.041Z","dependency_job_id":"e305451f-f7df-424e-bd94-b36c4fb12bbc","html_url":"https://github.com/fulifeng/Causal_Reading_Group","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/fulifeng%2FCausal_Reading_Group","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/fulifeng%2FCausal_Reading_Group/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/fulifeng%2FCausal_Reading_Group/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/fulifeng%2FCausal_Reading_Group/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/fulifeng","download_url":"https://codeload.github.com/fulifeng/Causal_Reading_Group/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246226974,"owners_count":20743862,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["causal-inference","causality","deep-learning","machine-learning","recommender-system"],"created_at":"2024-07-31T23:00:40.589Z","updated_at":"2025-03-29T18:31:20.621Z","avatar_url":"https://github.com/fulifeng.png","language":null,"funding_links":[],"categories":["Topics","Related Repo","其他_机器视觉","Related Repos","🚀 GitHub Repositories"],"sub_categories":["Arxiv","网络服务_其他","Research Paper","🌟 **Real-World Magic**"],"readme":"# Causal_Reading_Group\nThis is a list of papers about causality.\n\n## Table of Contents\n- [Survey paper](#survey-paper)\n- [Dataset](#dataset)\n- [Foundamental Causality](#foundamental-causality)\n- [Causality in Machine Learning](#causality-in-machine-learning)\n- [Causal Recommendation](#causal-recommendation)\n- [Causal Computer Vision](#causal-computer-vision)\n- [Causality in NLP](#causality-in-nlp)\n- [Causal Interpretability](#causal-interpretability)\n\n\n\n## Survey Paper\n- [Causal Machine Learning: A Survey and Open Problems] (https://arxiv.org/abs/2206.15475)(2022)\n- [D'ya like DAGs? A Survey on Structure Learning and Causal Discovery]() (2021)\n- [Causal Interpretability for Machine Learning -- Problems, Methods and Evaluation](https://arxiv.org/abs/2003.03934) (2020KDD)\n- [A Survey of Learning Causality with Data: Problems and Methods](https://arxiv.org/abs/1809.09337) (2020)\n- [A Survey on Causal Inference](https://arxiv.org/abs/2002.02770) (2020)\n\n## Dataset\n- [ACIC 2018 Data Challenge](https://www.cmu.edu/acic2018/data-challenge/index.html) (2018ACIC)\n\n## Foundamental Causality\n- [Estimating Identifiable Causal Effects on Markov Equivalence Class through Double Machine Learning]() (2021ICML)\n- [A Proxy Variable View of Shared Confounding]() (2021ICML)\n- [Valid Causal Inference with (Some) Invalid Instruments]() (2021ICML)\n- [Integer Programming for Causal Structure Learning in the Presence of Latent Variables]() (2021ICML)\n- [Operationalizing Complex Causes: A Pragmatic View of Mediation]() (2021ICML)\n- [Permutation Weighting]() (2021ICML)\n- [Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding]() (2021ICML)\n- [How and Why to Use Experimental Data to Evaluate Methods for Observational Causal Inference]() (2021ICML)\n- [Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction]() (2021ICML)\n- [Model-Free and Model-Based Policy Evaluation when Causality is Uncertain]() (2021ICML)\n- [Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning](https://arxiv.org/abs/1706.03461) (2019PNAS)\n- [Unit Selection Based on Counterfactual Logic](https://ftp.cs.ucla.edu/pub/stat_ser/r488.pdf) (2019IJCAI)\n- [Counterfactual regression with importance sampling weights](https://www.ijcai.org/Proceedings/2019/0815.pdf) (2019IJCAI)\n- [Orthogonal Random Forest for Causal Inference](https://arxiv.org/abs/1806.03467) (2019ICML)\n- [Estimation and Inference of Heterogeneous Treatment Effects using Random Forests](https://arxiv.org/abs/1510.04342) (2018JASA)\n- [Estimating individual treatment effect: generalization bounds and algorithms](https://arxiv.org/abs/1606.03976) (2017JMLR)\n- [Towards a learning theory of cause-effect inference](http://proceedings.mlr.press/v37/lopez-paz15.html) (2015ICML)\n\n## Causality in Machine Learning\n- [Causal Reasoning in Simulation for Structure and Transfer Learning of Robot Manipulation Policies](https://arxiv.org/abs/2103.16772) (2021ICRA)\n- [Generative Causal Explanations for Graph Neural Networks](https://arxiv.org/pdf/2104.06643.pdf)(2021ICML)\n- [Regularizing towards Causal Invariance: Linear Models with Proxies]() (2021ICML)\n- [Causality-aware counterfactual confounding adjustment as an alternative to linear residualization in anticausal prediction tasks based on linear learners]() (2021ICML)\n- [On Disentangled Representations Learned from Correlated Data]() (2021ICML)\n- [Modularity in Reinforcement Learning via Algorithmic Independence in Credit Assignment] (2021ICML)\n- [Domain Generalization using Causal Matching]() (2021ICML)\n- [Selecting Data Augmentation for Simulating Interventions]() (2021ICML)\n- [Necessary and sufficient conditions for causal feature selection in time series with latent common causes]() (2021ICML)\n- [Out-of-Distribution Generalization via Risk Extrapolation (REx)]() (2021ICML)\n- [Counterfactual Credit Assignment in Model-Free Reinforcement Learning]() (2021ICML)\n- [Size-Invariant Graph Representations for Graph Classification Extrapolations]() (2021ICML)\n- [Adapting Interactional Observation Embedding for Counterfactual Learning to Rank]() (2021SIGIR)\n- [Learning Individually Fair Classifier with Path-Specific Causal-Effect Constraint](http://proceedings.mlr.press/v130/chikahara21a.html)(2021AISTATS)\n- [Path-specific Counterfactual Fairness](https://ojs.aaai.org//index.php/AAAI/article/view/4777) (2019AAAI)\n- [Counterfactual Fairness](https://proceedings.neurips.cc/paper/2017/file/a486cd07e4ac3d270571622f4f316ec5-Paper.pdf) (2017 NIPS)\n- [A Causal View on Robustness of Neural Networks](https://arxiv.org/abs/2005.01095) (2020NeurIPS)\n- [An investigation of why overparameterization exacerbates spurious correlations](https://arxiv.org/pdf/2005.04345.pdf) (2020)\n- [Matching in Selective and Balanced Representation Space for Treatment Effects Estimation](https://dl.acm.org/doi/abs/10.1145/3340531.3412037) (2020CIKM)\n- [Improving the accuracy of medical diagnosis with causal machine learning](https://www.nature.com/articles/s41467-020-17419-7) (2020Nature Communication)\n- [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)\n- [Adapting Text Embeddings for Causal Inference](http://proceedings.mlr.press/v124/veitch20a.html) (2020UAI)\n- [Double/Debiased/Neyman Machine Learning of Treatment Effects](https://arxiv.org/abs/1701.08687) (2017American Economic Review)\n- [Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects](https://arxiv.org/pdf/1706.09523.pdf)(2020)\n- [Deep IV: A Flexible Approach for Counterfactual Prediction](http://proceedings.mlr.press/v70/hartford17a/hartford17a.pdf)(2017)\n- [Estimating individual treatment effect: generalization bounds and algorithms](https://arxiv.org/pdf/1606.03976.pdf)(2017)\n- [Causal Decision Trees](https://arxiv.org/pdf/1508.03812.pdf)(2020)\n- [Estimating Individual Treatment Effect in Observational Data Using Random Forest Methods](https://arxiv.org/pdf/1701.05306.pdf)(2018)\n\n## Causal Recommendation\n- [CausalInt: Causal Inspired Intervention for Multi-Domain Recommendation](2022KDD)\n- [Practical Counterfactual Policy Learning for Top-𝐾 Recommendations](https://www.csie.ntu.edu.tw/~cjlin/papers/counterfactual_topk/xcf.pdf)(2022KDD,IPS) \n- [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)\n- [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)\n- [Deconfounding Duration Bias in Watch-time Prediction for Video Recommendation.](https://arxiv.org/abs/2206.06003)(2022KDD,backdoor for duration bias)  \n- [Addressing Unmeasured Confounder for Recommendation with Sensitivity Analysis](http://staff.ustc.edu.cn/~hexn/papers/kdd22-deconfound-rec.pdf)(2022KDD,Adversarial IPS --\u003e unmeasured confounder)\n- [Mitigating Hidden Confounding Effects for Causal Recommendation](https://arxiv.org/abs/2205.07499)(2022arxive,frontdoor)\n- [Addressing Confounding Feature Issue for Causal Recommendation](https://arxiv.org/abs/2205.06532) (2022 TOIS,casual effect + pathe-specific causal effect)\n- [Tutorial-Causal Recommendation: Progresses and Future Directions](https://causalrec.github.io/) (2022 WWW tutorial) \n- [User-controllable Recommendation Against Filter Bubbles](https://arxiv.org/pdf/2204.13844.pdf)(2022 SIGIR, counterfactual)\n- [ESCM^2: Entire Space Counterfactual Multi-Task Model for Post-Click Conversion Rate Estimation](https://arxiv.org/abs/2204.05125) (2022 SIGIR, IPW+multitask)\n- [Causal Disentanglement for Semantics-Aware Intent Learning in Recommendation](https://ieeexplore.ieee.org/document/9736612) (2022 TKDE, deconfounding)\n- [Rating Distribution Calibration for Selection Bias Mitigation in Recommendations](https://dl.acm.org/doi/10.1145/3485447.3512078)(2022 www, selection bias)\n- [Unbiased Sequential Recommendation with Latent Confounders](https://dl.acm.org/doi/10.1145/3485447.3512092) (2022 WWW, IPS for seqrec)\n- [A Model-Agnostic Causal Learning Framework for Recommendation using Search Data](https://arxiv.org/pdf/2202.04514.pdf) (2022 WWW, instrumental variable)\n- [CausPref: Causal Preference Learning for Out-of-Distribution Recommendation](https://arxiv.org/pdf/2202.03984.pdf) (2022 WWW)\n- [Estimating Causal Effects of Multi-Aspect Online Reviews with Multi-Modal Proxies](https://arxiv.org/pdf/2112.10274.pdf) (2022 WSDM)\n- [Towards Unbiased and Robust Causal Ranking for Recommender Systems](https://dl.acm.org/doi/abs/10.1145/3488560.3498521) (2022 WSDM)\n- [Causal Inference for Visual Debiasing in Visually-Aware Recommendation](https://dl.acm.org/doi/pdf/10.1145/3383313.3412241) (2021 ACM MM)\n- [Mitigating Confounding Bias in Recommendation via Information Bottleneck](https://dl.acm.org/doi/10.1145/3460231.3474263) (2021 RecSys)\n- [Online Evaluation Methods for the Causal Effect of Recommendations](https://dl.acm.org/doi/fullHtml/10.1145/3460231.3474235) (2021 RecSys)\n- [Counterfactual Explainable Recommendation](https://arxiv.org/abs/2108.10539) (2021 CIKM) \n- [Counterfactual Review-based Recommendation](https://dl.acm.org/doi/abs/10.1145/3459637.3482244) (2021 CIKM)\n- [Top-N Recommendation with Counterfactual User Preference Simulation](https://arxiv.org/abs/2109.02444) (2021 CIKM)\n- [Causally Attentive Collaborative Filtering](https://dl.acm.org/doi/abs/10.1145/3459637.3482070) (2021 CIKM)\n- [CauSeR: Causal Session-based Recommendations for Handling Popularity Bias](https://dl.acm.org/doi/abs/10.1145/3459637.3482071) (2021 CIKM)\n- [CausCF: Causal Collaborative Filtering for Recommendation Effect Estimation](https://arxiv.org/pdf/2105.13881v1.pdf) (2021 CIKM)\n- [A Semi-Synthetic Dataset Generation Framework for Causal Inference in Recommender Systems](https://openreview.net/forum?id=OqPBtOcFJt-) (2021)\n- [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)\n- [Deconfounded Recommendation for Alleviating Bias Amplification](https://arxiv.org/abs/2105.10648) (2021 KDD)\n- [Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System](https://arxiv.org/abs/2010.15363) (2021 KDD)\n- [Causal Intervention for Leveraging Popularity Bias in Recommendation](https://arxiv.org/abs/2105.06067)(2021 SIGIR)\n- [Clicks can be Cheating: Counterfactual Recommendation for Mitigating Clickbait Issue](https://arxiv.org/pdf/2009.09945.pdf) (2021SIGIR)\n- [CauseRec: Counterfactual User Sequence Synthesis for Sequential Recommendation](https://dl.acm.org/doi/pdf/10.1145/3404835.3462908) (2021SIGIR)\n- [Personalized Counterfactual Fairness in Recommendation](https://arxiv.org/pdf/2105.09829.pdf) (2021SIGIR)\n- [Counterfactual Data-Augmented Sequential Recommendation](https://dl.acm.org/doi/10.1145/3404835.3462855) (2021 SIGIR)\n- [Counterfactual Reward Modification for Streaming Recommendation with Delayed Feedback]() (2021SIGIR)\n- [Counterfactual Explanations for Neural Recommenders](https://arxiv.org/abs/2105.05008) (2021SIGIR)\n- [The Simpson’s Paradox in the Offline Evaluation of Recommendation Systems](https://arxiv.org/pdf/2104.08912.pdf)(2021)\n- [PRINCE: Provider-side Interpretability with Counterfactual Explanations in Recommender Systems](https://arxiv.org/abs/1911.08378v4) (2020WSDM)\n- [Counterfactual Prediction for Bundle Treatment](https://proceedings.neurips.cc/paper/2020/file/e430ad64df3de73e6be33bcb7f6d0dac-Paper.pdf) (2020NeurIPS)\n- [Adversarial Counterfactual Learning and Evaluation for Recommender System](https://papers.nips.cc/paper/2020/file/9cd013fe250ebffc853b386569ab18c0-Paper.pdf) (2020NeurIPS)\n- [Information Theoretic Counterfactual Learning from Missing-Not-At-Random Feedback](https://papers.nips.cc/paper/2020/file/13f3cf8c531952d72e5847c4183e6910-Paper.pdf) (2020NeurIPS)\n- [Learning Stable Graphs from Multiple Environments with Selection Bias](https://dl.acm.org/doi/abs/10.1145/3394486.3403270) (2020KDD)\n- [Causal Inference for Recommender Systems](https://dl.acm.org/doi/10.1145/3383313.3412225) (2020 RecSys)\n- [Debiasing Item-to-Item Recommendations With Small Annotated Datasets](https://dl.acm.org/doi/10.1145/3383313.3412265) (2020 RecSys)\n- [Deconfounding User Satisfaction Estimation from Response Rate Bias](https://dl.acm.org/doi/10.1145/3383313.3412208) (2020 RecSys)\n- [Unbiased Learning for the Causal Effect of Recommendation](https://dl.acm.org/doi/10.1145/3383313.3412261) (2020 RecSys)\n- [Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback](https://arxiv.org/pdf/1909.03601.pdf) (2020WSDM)\n- [A General Framework for Counterfactual Learning-to-Rank](http://www.cs.cornell.edu/people/tj/publications/agarwal_etal_19b.pdf) (2019SIGIR)\n- [Doubly Robust Joint Learning for Recommendation on Data Missing Not at Random](http://proceedings.mlr.press/v97/wang19n/wang19n.pdf) (2019ICML)\n- [Causal Embeddings for Recommendation: An Extended Abstract](https://www.ijcai.org/Proceedings/2019/0870.pdf) (2019IJCAI)\n- [Unbiased Learning to Rank with Unbiased Propensity Estimation](https://arxiv.org/pdf/1804.05938.pdf) (2018SIGIR)\n- [Recommendations as Treatments: Debiasing Learning and Evaluation](https://arxiv.org/abs/1602.05352) (2016ICML)\n- [Estimating the Causal Impact of Recommendation Systems from Observational Data](https://arxiv.org/abs/1510.04342) (2015ACMEC)\n- [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)\n\n\n## Causal Computer Vision\n- [Interventional Video Grounding with Dual Contrastive Learning](https://arxiv.org/pdf/2106.11013.pdf) (2021CVPR)\n- [Deconfounded Video Moment Retrieval with Causal Intervention](https://arxiv.org/pdf/2106.01534.pdf) (2021SIGIR)\n- [Causal Attention for Vision-Language Tasks](https://arxiv.org/abs/2103.03493) (2021CVPR)\n- [Deconfounded Image Captioning: A Causal Retrospect](https://arxiv.org/abs/2003.03923) \n- [Counterfactual VQA: A Cause-Effect Look at Language Bias](https://arxiv.org/abs/2006.04315) (2021CVPR)\n- [Visual Commonsense R-CNN](https://arxiv.org/abs/2002.12204) (2020CVPR)\n- [More Grounded Image Captioning by Distilling Image-Text Matching Model](https://arxiv.org/abs/2004.00390) (2020CVPR)\n- [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)\n- [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)\n- [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)\n- [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)\n- [Counterfactual Vision-and-Language Navigation via Adversarial Path Sampling](https://arxiv.org/abs/1911.07308) (2020ECCV)\n\n## Causality in NLP\n- [Uncovering Main Causalities for Long-tailed Information Extraction](https://arxiv.org/pdf/2109.05213v1.pdf) (2021EMNLP)\n- [Empowering Language Understanding with Counterfactual Reasoning](https://arxiv.org/pdf/2106.03046.pdf) (2021ACL)\n- [Everything Has a Cause: Leveraging Causal Inference in Legal Text Analysis](https://arxiv.org/abs/2104.09420) (2021NAACL)\n- [How to make causal inferences using texts](https://arxiv.org/pdf/1802.02163.pdf) (2018arxiv)\n- [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)\n- [Causal inference of script knowledge](https://www.aclweb.org/anthology/2020.emnlp-main.612.pdf) (2020EMNLP)\n- [De-Biased Court’s View Generation with Causality](https://www.aclweb.org/anthology/2020.emnlp-main.56.pdf) (2020EMNLP)\n- [Counterfactual Generator: A Weakly-Supervised Method for Named Entity Recognition](https://github.com/xijiz/cfgen/blob/master/docs/cfgen.pdf) (2020EMNLP)\n- [Counterfactual Off-Policy Training for Neural Dialogue Generation](https://arxiv.org/abs/2004.14507) (2020EMNLP)\n- [Identifying Spurious Correlations for Robust Text Classification](https://arxiv.org/pdf/2010.02458.pdf) (2020EMNLP)\n- [Feature Selection as Causal Inference: Experiments with Text Classification](https://www.aclweb.org/anthology/K17-1018/) (2017CoNLL)\n\n\n\n## Causal Interpretability\n### Causal Discovery\n- [OrphicX: A Causality-Inspired Latent Variable Model for Interpreting Graph Neural Networks](https://arxiv.org/pdf/2203.15209.pdf)(2022CVPR)\n- [CausalVAE: Disentangled Representation Learning via Neural Structural Causal Models](https://arxiv.org/pdf/2004.08697.pdf)(2021CVPR)\n- [Disentangled Generative Causal Representation Learning](https://arxiv.org/pdf/2010.02637.pdf)\n- [Causal Inference with Deep Causal Graphs](https://arxiv.org/abs/2006.08380)\n- [Causal Discovery with Reinforcement Learning](https://arxiv.org/abs/1906.04477)(2020ICLR)\n- [Causal Discovery from Incomplete Data: A Deep Learning Approach](https://arxiv.org/pdf/2001.05343.pdf)(2020AAAI)\n- [A Graph Autoencoder Approach to Causal Structure Learning](https://arxiv.org/abs/1911.07420) (2019-NeurIPS)\n- [CXPlain: Causal Explanations for Model Interpretation under Uncertainty](https://papers.nips.cc/paper/2019/file/3ab6be46e1d6b21d59a3c3a0b9d0f6ef-Paper.pdf) (2019-NeurIPS)\n- [Neural Network Attributions: A Causal Perspective](https://arxiv.org/abs/1902.02302) (2019-ICML)\n- [Building Causal Graphs from Medical Literature and Electronic Medical Records](https://ojs.aaai.org//index.php/AAAI/article/view/3902)(2019-AAAI)\n- [Explaining Deep Learning Models Using Causal Inference](https://arxiv.org/abs/1811.04376) (2018)\n- [Neural Relational Inference for Interacting Systems](https://arxiv.org/pdf/1802.04687.pdf) (2018-ICML)\n- [Learning Independent Causal Mechanisms](https://arxiv.org/abs/1712.00961) (2018-ICML)\n- [DAGs with NO TEARS: Continuous Optimization for Structure Learning](https://papers.nips.cc/paper/2018/file/e347c51419ffb23ca3fd5050202f9c3d-Paper.pdf) (2018-NeurIPS)\n- [A Causal Framework for Explaining the Predictions of Black-box Sequence-to-sequence Models](https://www.aclweb.org/anthology/D17-1042/) (2017-EMNLP)\n\n### Causal Intervention\n- [Causal Intervention for Weakly-Supervised Semantic Segmentation](https://arxiv.org/abs/2009.12547) (2020NeurIPS)\n- [Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect](https://arxiv.org/abs/2009.12991) (2020NeurIPS)\n- [Interventional Few-Shot Learning](https://arxiv.org/abs/2009.13000) (2020NeurIPS)\n- [GAN Disssertion: Visualizing and Understnding Generative Adversarial Networks](https://arxiv.org/abs/1811.10597) (2018ICLR)\n- [Causal Learning and Explanation of Deep Neural Networks via Autoencoded Activations](https://arxiv.org/abs/1802.00541) (2018)\n\n\n### Counterfactual Interpretability\n- [Model-Based Counterfactual Synthesizer for Interpretation](https://arxiv.org/pdf/2106.08971.pdf) (2021 KDD)\n- [Counterfactual Explanations for Neural Recommenders](https://arxiv.org/abs/2105.05008) (2021SIGIR)\n- [Algorithmic Recourse: from Counterfactual Explanations to Interventions](https://dl.acm.org/doi/abs/10.1145/3442188.3445899) (2021FAT)\n- [CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks](https://arxiv.org/pdf/2102.03322.pdf) (2021)\n- [Delta-CLUE Divere Sets of Explanations for Uncertainty Estimates](https://arxiv.org/abs/2104.06323) (2021ICLR workshop)\n- [Explaining Deep Graph Networks with Molecular Counterfactuals](https://arxiv.org/abs/2011.05134) (2020NeurIPS)\n- [Learning the Difference that Makes a Difference with Counterfactually-augmented Data](https://arxiv.org/abs/1909.12434) (2020ICLR)\n- [Counterfactual Generator: A Weakly-Supervised Method for Named Entity Recognition](https://www.aclweb.org/anthology/2020.emnlp-main.590/) (2020EMNLP)\n- [Counterfactual Visual Explanations](https://arxiv.org/pdf/1904.07451.pdf) (2019ICML)\n- [Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers](https://arxiv.org/abs/1912.03277) (2019NeurIPS)\n- [Explaining Image Classifiers by Counterfactual Generation](https://arxiv.org/abs/1807.08024) (2019ICLR)\n- [Interpretable Counterfactual Explanations Guided by Prototypes](https://arxiv.org/abs/1907.02584) (2019)\n- [Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations](https://arxiv.org/abs/1905.07697) (2019FAT)\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffulifeng%2FCausal_Reading_Group","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffulifeng%2FCausal_Reading_Group","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffulifeng%2FCausal_Reading_Group/lists"}