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Causal in Vision and NLP

Relative repo

Awesome Causality. Which compile major resources related to causality in one place under different categories.

awesome-causality-algorithms. Which is an index of algorithms for learning causality with data.

awesome-causality-data Which is an index of datasets that can be used for learning causality.

Awesome-Causality-in-CV. Which is a curated list of causality in computer vision.

Awesome-Causal-Vision Which is also a list of research papers in exploring causality in vision.

Survey

  1. A Survey of Learning Causality with Data: Problems and Methods. Guo, R., Cheng, L., Li, J., Hahn, P. R., & Liu, H. (2020). A survey of learning causality with data: Problems and methods. ACM Computing Surveys (CSUR) [Paper] [cite:74] ✔️

  2. Causal inference Kuang, K., Li, L., Geng, Z., Xu, L., Zhang, K., Liao, B., ... & Jiang, Z. (2020). Causal inference. Engineering. [Paper] [Alternative] [cite:15]✔️

Paper with code

I apologize for the non-accuracy cite information.

2023

  1. Good is Bad: Causality Inspired Cloth-Debiasing for Cloth-Changing Person Re-Identification Yang Z, Lin M, Zhong X, Wu Y , and Wang Z. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023. (CVPR 2023) [Paper] [Code] [supp] [cite:1]

  2. Discovering the Real Association: Multimodal Causal Reasoning in Video Question Answering Zang C, Wang H, Pei M, and Liang W. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023. (CVPR 2023) [Paper] [Code] [supp] [cite:0]

  3. Learning Distortion Invariant Representation for Image Restoration From a Causality Perspective Li X, Li B, Jin X, Lan C, and Chen Z. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023. (CVPR 2023) [Paper] [arXiv] [Code] [supp] [cite:0]

  4. Demystifying Causal Features on Adversarial Examples and Causal Inoculation for Robust Network by Adversarial Instrumental Variable Regression Kim J, Lee B K, Ro Y M. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023. (CVPR 2023) [Paper] [arXiv] [supp] [cite:1]

  5. Causally-Aware Intraoperative Imputation for Overall Survival Time Prediction Li X, Qian X, Liang L, et al. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023. (CVPR 2023) [Paper] [supp] [cite:0]

  6. Layout-Based Causal Inference for Object Navigation Zhang S, Song X, Li W, et al. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023. (CVPR 2023) [Paper] [Code] [supp] [cite:1]

  7. Meta-Causal Learning for Single Domain Generalization Chen J, Gao Z, Wu X, et al. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023. (CVPR 2023) [Paper] [arXiv] [supp] [cite:2]

  8. CafeBoost: Causal Feature Boost To Eliminate Task-Induced Bias for Class Incremental Learning Qiu B, Li H, Wen H, et al. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023. (CVPR 2023) [Paper] [supp] [cite:0]

  9. Multi-View Adversarial Discriminator: Mine the Non-Causal Factors for Object Detection in Unseen Domains Xu M, Qin L, Chen W, et al. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023. (CVPR 2023) [Paper] [arXiv] [Code] [cite:0]

  10. An Actor-Centric Causality Graph for Asynchronous Temporal Inference in Group Activity Xie Z, Gao T, Wu K, Chang J. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023. (CVPR 2023) [Paper] [cite:0]

  11. Masked Images Are Counterfactual Samples for Robust Fine-Tuning In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023. (CVPR 2023) [Paper] [arXiv] [supp] [Code] [cite:1]

  12. Adversarial Counterfactual Visual Explanations Jeanneret G, Simon L, Jurie F. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023. (CVPR 2023) [Paper] [arXiv] [supp] [Code] [cite:1]

  13. GaitGCI: Generative Counterfactual Intervention for Gait Recognition Dou H, Zhang P, Su W, et al. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023. (CVPR 2023) [Paper] [supp] [cite:2]

  14. Grounding Counterfactual Explanation of Image Classifiers to Textual Concept Space Kim S, Oh J, Lee S, et al. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023. (CVPR 2023) [Paper] [supp] [cite:0]

  15. OCTET: Object-Aware Counterfactual Explanations Zemni M, Chen M, Zablocki É, et al. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023. (CVPR 2023) [Paper] [arXiv] [supp] [Code] [cite:2]

  16. Interventional Bag Multi-Instance Learning On Whole-Slide Pathological Images Lin T, Yu Z, Hu H, et al. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023. (CVPR 2023) [Paper] [arXiv] [supp] [Code] [cite:3]

2022

  1. Do learned representations respect causal relationships? Wang L, Boddeti V.N, In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022. (CVPR 2022) [Paper] [arXiv] [Code] [supp] [cite:0]

  2. OrphicX: A Causality-Inspired Latent Variable Model for Interpreting Graph Neural Networks Lin W, Lan H, Wang H, et.al, In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022. (CVPR 2022) [Paper] [arXiv] [[Code]] [supp] [cite:2]

  3. C-CAM: Causal CAM for Weakly Supervised Semantic Segmentation on Medical Image Chen Z, Tian Z, Zhu J, et.al, In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022. (CVPR 2022) [Paper] [Code] [cite:0]

  4. Causality Inspired Representation Learning for Domain Generalization Lv F, Liang J, Li S, et.al, In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022. (CVPR 2022) [Paper] [arXiv] [[Code]] [supp] [cite:0]

  5. Out-of-Distribution Generalization With Causal Invariant Transformations Wang R, Yi M, Chen Z, et.al, In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022. (CVPR 2022) [Paper] [arXiv] [[Code]] [supp] [cite:0]

  6. Show, Deconfound and Tell: Image Captioning With Causal Inference Liu B, Wang D, Yang X, et.al, In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022. (CVPR 2022) [Paper] [[Code]] [supp] [cite:0]

  7. Towards Robust and Adaptive Motion Forecasting: A Causal Representation Perspective Liu Y, Cadei R, Schweizer J, et.al, In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022. (CVPR 2022) [Paper] [arXiv] [Code] [supp] [cite:0]

  8. Causal Transportability for Visual Recognition Mao C, Xia K, Wang J, et.al, In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022. (CVPR 2022) [Paper] [arXiv] [Code] [cite:0]

  9. Contextual Debiasing for Visual Recognition With Causal Mechanisms Liu R, Liu H, Li G, et.al, In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022. (CVPR 2022) [Paper] [Code] [cite:0]

  10. Evaluating and Mitigating Bias in Image Classifiers: A Causal Perspective Using Counterfactuals Dash S, Balasubramanian V.N, Sharma A, et.al, In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 2022. (WACV 2022) [Paper] [arXiv] [supp] [cite:0]

  11. Deep Multi-Modal Structural Equations For Causal Effect Estimation With Unstructured Proxies Shachi Deshpande, Kaiwen Wang, Dhruv Sreenivas, Zheng Li, Volodymyr Kuleshov, Thirty-Sixth Conference on Neural Information Processing Systems. 2022. (NIPS 2022) [Paper] [arXiv] [review] [cite:1]

  12. Disentangling Causal Effects from Sets of Interventions in the Presence of Unobserved Confounders Olivier Jeunen, Ciarán M. Lee, Rishabh Mehrotra, Mounia Lalmas, Thirty-Sixth Conference on Neural Information Processing Systems. 2022. (NIPS 2022) [Paper] [arXiv] [review] [Code] [cite:0]

  13. Contrastive Graph Structure Learning via Information Bottleneck for Recommendation Chunyu Wei, Jian Liang, Di Liu, Fei Wang, Thirty-Sixth Conference on Neural Information Processing Systems. 2022. (NIPS 2022) [Paper] [review] [Code] [cite:0]

  14. CEBaB: Estimating the Causal Effects of Real-World Concepts on NLP Model Behavior Eldar David Abraham, Karel D'Oosterlinck, Amir Feder, Yair Ori Gat, Atticus Geiger, Christopher Potts, Roi Reichart, Zhengxuan Wu, Thirty-Sixth Conference on Neural Information Processing Systems. 2022. (NIPS 2022) [Paper] [review] [Code] [cite:0]

  15. An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effects Thanh Vinh Vo, Arnab Bhattacharyya, Young Lee, Tze-Yun Leong, Thirty-Sixth Conference on Neural Information Processing Systems. 2022. (NIPS 2022) [Paper] [review] [Code] [cite:0]

  16. Generalization Bounds for Estimating Causal Effects of Continuous Treatments Xin Wang, Shengfei Lyu, Xingyu Wu, Tianhao Wu, Huanhuan Chen, Thirty-Sixth Conference on Neural Information Processing Systems. 2022. (NIPS 2022) [Paper] [review] [Code] [cite:0]

  17. Falsification before Extrapolation in Causal Effect Estimation Zeshan Hussain, Michael Oberst, Ming-Chieh Shih, David Sontag, Thirty-Sixth Conference on Neural Information Processing Systems. 2022. (NIPS 2022) [Paper] [review] [Code] [cite:0]

  18. Learning Individualized Treatment Rules with Many Treatments: A Supervised Clustering Approach Using Adaptive Fusion Haixu Ma, Donglin Zeng, Yufeng Liu, Thirty-Sixth Conference on Neural Information Processing Systems. 2022. (NIPS 2022) [Paper] [review] [Code] [cite:0]

  19. Partial Identification of Treatment Effects with Implicit Generative Models Vahid Balazadeh Meresht, Vasilis Syrgkanis, Rahul G Krishnan, Thirty-Sixth Conference on Neural Information Processing Systems. 2022. (NIPS 2022) [Paper] [review] [Code] [cite:0]

  20. Debiased Causal Tree: Heterogeneous Treatment Effects Estimation with Unmeasured Confounding Caizhi Tang, Huiyuan Wang, Xinyu Li, Qing Cui, Ya-Lin Zhang, Feng Zhu, Longfei Li, JUN ZHOU, Linbo Jiang, Thirty-Sixth Conference on Neural Information Processing Systems. 2022. (NIPS 2022) [Paper] [review] [Code] [cite:0]

  21. Sample Constrained Treatment Effect Estimation Raghavendra Addanki, David Arbour, Tung Mai, Cameron N Musco, Anup Rao, Thirty-Sixth Conference on Neural Information Processing Systems. 2022. (NIPS 2022) [Paper] [review] [Code(ing)] [cite:0]

  22. Transfer Learning on Heterogeneous Feature Spaces for Treatment Effects Estimation Ioana Bica, Mihaela van der Schaar, Thirty-Sixth Conference on Neural Information Processing Systems. 2022. (NIPS 2022) [Paper] [review] [Code] [cite:0]

  23. Towards Unbiased Visual Emotion Recognition via Causal Intervention Yuedong Chen, Xu Yang, Tat-Jen Cham, Jianfei Cai, ACM Multimedia. 2022. (ACM MM 2022) [Paper] [Code] [cite:5]

  24. Towards Causality Inference for Very Important Person Localization Xiao Wang, Zheng Wang, Wu Liu, Xin Xu, Qijun Zhao, Shin'ichi Satoh, ACM Multimedia. 2022. (ACM MM 2022) [Paper] [cite:0]

  25. Counterfactual Reasoning for Out-of-distribution Multimodal Sentiment Analysis Teng Sun, Wenjie Wang, Liqiang Jing, Yiran Cui, Xuemeng Song, Liqiang Nie, ACM Multimedia. 2022. (ACM MM 2022) [Paper] [Code] [cite:3]

  26. Towards Counterfactual Image Manipulation via CLIP Yingchen Yu, Fangneng Zhan, Rongliang Wu, Jiahui Zhang, Shijian Lu, Miaomiao Cui, Xuansong Xie, Xian-Sheng Hua, Chunyan Miao, ACM Multimedia. 2022. (ACM MM 2022) [Paper] [Code] [cite:9]

  27. Synthesizing Counterfactual Samples for Effective Image-Text Matching Hao Wei, Shuhui Wang, Xinzhe Han, Zhe Xue, Bin Ma, Xiaoming Wei, Xiaolin Wei, ACM Multimedia. 2022. (ACM MM 2022) [Paper] [Code] [cite:0]

  28. Counterexample Contrastive Learning for Spurious Correlation Elimination Jinqiang Wang, Rui Hu, Chaoquan Jiang, Rui Hu, Jitao Sang, ACM Multimedia. 2022. (ACM MM 2022) [Paper] [cite:0]

  29. Counterfactually Measuring and Eliminating Social Bias in Vision-Language Pre-training Models Yi Zhang, Junyang Wang, Jitao Sang, ACM Multimedia. 2022. (ACM MM 2022) [Paper] [cite:1]

  30. Deconfounding Physical Dynamics with Global Causal Relation and Confounder Transmission for Counterfactual Prediction Zongzhao Li, Xiangyu Zhu, Zhen Lei, Zhaoxiang Zhang, Association for the Advancement of Artificial Intelligence. 2022. (AAAI 2022) [Paper] [Code] [cite:0]

  31. AutoCFR: Learning to Design Counterfactual Regret Minimization Algorithms Hang Xu, Kai Li, Haobo Fu, Qiang Fu, Junliang Xing, Association for the Advancement of Artificial Intelligence. 2022. (AAAI 2022) [Paper] [Code] [cite:1]

  32. FOCUS: Flexible Optimizable Counterfactual Explanations for Tree Ensembles HAna Lucic, Harrie Oosterhuis, Hinda Haned, Maarten de Rijke, Association for the Advancement of Artificial Intelligence. 2022. (AAAI 2022) [Paper] [Code] [cite:49]

  33. Enhancing Counterfactual Classification Performance via Self-Training Ruijiang Gao, Max Biggs, Wei Sun, Ligong Han, Association for the Advancement of Artificial Intelligence. 2022. (AAAI 2022) [Paper] [Code] [cite:0]

  34. Achieving Counterfactual Fairness for Causal Bandit Wen Huang, Lu Zhang, Xintao Wu, Association for the Advancement of Artificial Intelligence. 2022. (AAAI 2022) [Paper] [cite:4]

  35. Diverse, Global and Amortised Counterfactual Explanations for Uncertainty Estimates Dan Ley, Umang Bhatt, Adrian Weller, Association for the Advancement of Artificial Intelligence. 2022. (AAAI 2022) [Paper] [cite:6]

  36. Unsupervised Editing for Counterfactual Stories Jiangjie Chen, Chun Gan, Sijie Cheng, Hao Zhou, Yanghua Xiao, Lei Li, Association for the Advancement of Artificial Intelligence. 2022. (AAAI 2022) [Paper] [Code] [cite:4]

  37. Debiasing NLU Models via Causal Intervention and Counterfactual Reasoning Bing Tian, Yixin Cao, Yong Zhang, Chunxiao Xing, Association for the Advancement of Artificial Intelligence. 2022. (AAAI 2022) [Paper] [cite:2]

  38. Causal Intervention for Subject-Deconfounded Facial Action Unit Recognition Yingjie Chen, Diqi Chen, Tao Wang, Yizhou Wang, Yun Liang, Association for the Advancement of Artificial Intelligence. 2022. (AAAI 2022) [Paper] [cite:5]

  39. Latent Space Explanation by Intervention Itai Gat, Guy Lorberbom, Idan Schwartz, Tamir Hazan, Association for the Advancement of Artificial Intelligence. 2022. (AAAI 2022) [Paper] [cite:3]

  40. Interventional Multi-Instance Learning with Deconfounded Instance-Level Prediction Tiancheng Lin, Hongteng Xu, Canqian Yang, Yi Xu, Association for the Advancement of Artificial Intelligence. 2022. (AAAI 2022) [Paper] [cite:4]

  41. Efficient Causal Structure Learning from Multiple Interventional Datasets with Unknown Targets Tiancheng Lin, Hongteng Xu, Canqian Yang, Yi Xu, Association for the Advancement of Artificial Intelligence. 2022. (AAAI 2022) [Paper] [cite:0]

  42. A Causal Debiasing Framework for Unsupervised Salient Object Detection Xiangru Lin, Ziyi Wu, Guanqi Chen, Guanbin Li, Yizhou Yu, Association for the Advancement of Artificial Intelligence. 2022. (AAAI 2022) [Paper] [cite:4]

  43. A Causal Inference Look at Unsupervised Video Anomaly Detection Xiangru Lin, Yuyang Chen, Guanbin Li, Yizhou Yu, Association for the Advancement of Artificial Intelligence. 2022. (AAAI 2022) [Paper] [cite:6]

  44. Information-Theoretic Bias Reduction via Causal View of Spurious Correlation Seonguk Seo, Joon-Young Lee, Bohyung Han, Association for the Advancement of Artificial Intelligence. 2022. (AAAI 2022) [Paper] [cite:7]

  45. DeepVisualInsight: Time-Travelling Visualization for Spatio-Temporal Causality of Deep Classification Training Xianglin Yang, Yun Lin, Ruofan Liu, Zhenfeng He, Chao Wang, Jin Song Dong, Hong Mei, Association for the Advancement of Artificial Intelligence. 2022. (AAAI 2022) [Paper] [cite:3]

  46. On Testing for Discrimination Using Causal Models Xianglin Yang, Yun Lin, Ruofan Liu, Zhenfeng He, Chao Wang, Jin Song Dong, Hong Mei, Association for the Advancement of Artificial Intelligence. 2022. (AAAI 2022) [Paper] [cite:1]

  47. Reasoning about Causal Models with Infinitely Many Variables Joseph Y. Halpern, Spencer Peters, Association for the Advancement of Artificial Intelligence. 2022. (AAAI 2022) [Paper] [cite:2]

  48. A Hybrid Causal Structure Learning Algorithm for Mixed-Type Data Yan Li, Rui Xia, Chunchen Liu, Liang Sun, Association for the Advancement of Artificial Intelligence. 2022. (AAAI 2022) [Paper] [Code] [cite:1]

  49. On Causally Disentangled Representations Abbavaram Gowtham Reddy, Benin Godfrey L, Vineeth N. Balasubramanian, Association for the Advancement of Artificial Intelligence. 2022. (AAAI 2022) [Paper] [Code] [cite:9]

  50. VACA: Designing Variational Graph Autoencoders for Causal Queries Pablo Sánchez-Martín, Miriam Rateike, Isabel Valera, Association for the Advancement of Artificial Intelligence. 2022. (AAAI 2022) [Paper] [Code] [cite:1]

  51. CausalGNN: Causal-Based Graph Neural Networks for Spatio-Temporal Epidemic Forecasting Lijing Wang, Aniruddha Adiga, Jiangzhuo Chen, Adam Sadilek, Srinivasan Venkatramanan, Madhav V. Marathe, Association for the Advancement of Artificial Intelligence. AISI Track. 2022. (AAAI 2022) [Paper] [cite:7]

  52. Counterfactual Intervention Feature Transfer for Visible-Infrared Person Re-identification Xulin Li, Yan Lu, Bin Liu, Yating Liu, Guojun Yin, Qi Chu, Jinyang Huang, Feng Zhu, Rui Zhao, Nenghai Yu, European Conference on Computer Vision. 2022. (ECCV 2022) [Paper] [cite:0]

  53. STEEX: Steering Counterfactual Explanations with Semantics Paul Jacob, Éloi Zablocki, Hédi Ben-Younes, Mickaël Chen, Patrick Pérez, Matthieu Cord, European Conference on Computer Vision. 2022. (ECCV 2022) [Paper] [Code] [cite:7]

  54. Making Heads or Tails: Towards Semantically Consistent Visual Counterfactuals Simon Vandenhende, Dhruv Mahajan, Filip Radenovic, Deepti Ghadiyaram, European Conference on Computer Vision. 2022. (ECCV 2022) [Paper] [Code] [cite:5]

  55. Meta-Causal Feature Learning for Out-of-Distribution Generalization Yuqing Wang, Xiangxian Li, Zhuang Qi, Jingyu Li, Xuelong Li, Xiangxu Meng, Lei Meng, European Conference on Computer Vision. Workshop. 2022. (ECCV.W 2022) [Paper] [cite:1]

  56. Counterfactual Fairness for Facial Expression Recognition Jiaee Cheong, Sinan Kalkan, Hatice Gunes:, European Conference on Computer Vision. Workshop. 2022. (ECCV.W 2022) [Paper] [cite:1]

2021

  1. Causal Attention for Vision-Language Tasks. Yang, Xu, Zhang,H et al., In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021. (CVPR 2021) [Paper] [arXiv] [Code] [cite:3] ✔️

  2. Distilling Causal Effect of Data in Class-Incremental Learning. Hu, X., Tang, K., Miao, C., Hua, X. S., & Zhang, H. (2021)., In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021. (CVPR 2021) [arXiv] [Code] [cite:8] ⏲️

  3. Counterfactual Zero-Shot and Open-Set Visual Recognition. Yue, Z., Wang, T., Zhang, H., Sun, Q., & Hua, X. S., In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021. (CVPR 2021) [Paper] [arXiv] [Code] [cite:2]

  4. Counterfactual VQA: A Cause-Effect Look at Language Bias. Niu, Y., Tang, K., Zhang, H., Lu, Z., Hua, X. S., & Wen, J. R., In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021. (CVPR 2021) [Paper] [arXiv] [Code] [cite:16]

  5. CausalVAE: Disentangled Representation Learning via Neural Structural Causal Models. Yang, M., Liu, F., Chen, Z., Shen, X., Hao, J., & Wang, J., In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021. (CVPR 2021) [Paper] [arXiv] [Code] [Slides] [cite:2]

  6. Generative Interventions for Causal Learning. Mao, C., Gupta, A., Cha, A., Wang, H., Yang, J., & Vondrick, C., In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021. (CVPR 2021) [Paper] [arXiv] [Code] [cite:1]

  7. ACRE: Abstract Causal REasoning Beyond Covariation. Zhang, C., Jia, B., Edmonds, M., Zhu, S. C., & Zhu, Y., In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021. (CVPR 2021) [Paper] [arXiv] [Code] [supp] [Blog] [cite:1]

  8. Believe It or Not: Counterfactual Bias Manipulation in Visual Question Answering. Yang,M., Deng,C., Yan,J., In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021. (CVPR 2021) [No paper]

  9. Representation Learning via Invariant Causal Mechanisms. Mitrovic, J., McWilliams, B., Walker, J., Buesing, L., & Blundell, C. , In International Conference on Learning Representations. (ICLR2021) [Paper] [arXiv] [Review] [supp] [cite:6]

  10. Counterfactual Generative Networks. Sauer, A., & Geiger, A., In International Conference on Learning Representations. (ICLR2021) [Paper] [arXiv] [Code] [Blog] [Review] [cite:2]

  11. Selectivity considered harmful: evaluating the causal impact of class selectivity in DNNs. Leavitt, M. L., & Morcos, A., In International Conference on Learning Representations. (ICLR2021) [Paper] [arXiv] [Review] [cite:8]

  12. CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning. Ahmed, O., Träuble, F., Goyal, A., Neitz, A., Bengio, Y.,etc , In International Conference on Learning Representations. (ICLR2021) [Paper] [arXiv] [Code] [Blog] [Doc] [Review] [cite:9]

  13. （For COVID-19）ANOCE: Analysis of Causal Effects with Multiple Mediators via Constrained Structural Learning. Cai, H., Song, R., Lu, W., & Seminar, N. C. S. U., In International Conference on Learning Representations. (ICLR2021) [Paper] [code] [Sides] [Review] [supp] [cite:0]

  14. Interpretable Models for Granger Causality Using Self-explaining Neural Networks. Marcinkevičs, R., & Vogt, J. E., In International Conference on Learning Representations. (ICLR2021) [Paper] [arXiv] [Code] [Review] [Video] [suppp] [cite:1]

  15. Dependent Multi-Task Learning with Causal Intervention for Image Captioning. Chen, W., Tian, J., Fan, C., He, H., & Jin, Y., In International Journal of Applied and Creative Arts (IJACA2021) [Paper] [arXiv] [cite:0]

  16. Disentangled Generative Causal Representation Learning. Shen, X., Liu, F., Dong, H., Lian, Q., Chen, Z., & Zhang, T., arXiv preprint arXiv:2010.02637. (Rejectded by ICLR21) [Paper] [arXiv] [Code] [Review] [cite:2]

  17. Adversarial Visual Robustness by Causal Intervention. Tang, K., Tao, M., & Zhang, H. (2021). arXiv preprint arXiv:2106.09534. [[06.17]] [arXiv] [Code] [cite:0]

  18. Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identification. Rao, Yongming, et al. ICCV (2021). [Paper] [arXiv] [Code] [suppp] [cite:1]

  19. Causal Effect Inference for Structured Treatments. Kaddour, Jean, et al. Thirty-Fifth Conference on Neural Information Processing Systems. [Paper] [arXiv] [Code] [cite:0]

  20. Causal attention for unbiased visual recognition. Wang, Tan, et al. Proceedings of the IEEE/CVF International Conference on Computer Vision. (ICCV2021). [Paper] [arXiv] [Code] [cite:3]

  21. Spatial-temporal Causal Inference for Partial Image-to-video Adaptation. Chen, Jin, et al. Proceedings of the AAAI Conference on Artificial Intelligence. (AAAI2021). [Paper] [[Code]] [cite:0]

2020

  1. Unbiased scene graph generation from biased training. Tang, Kaihua, et al. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020. (CVPR 2020) [Paper] [arXiv] [Code] [supp] [cite:57]

  2. Deconfounded Image Captioning: A Causal Retrospect. Yang, X., Zhang, H., & Cai, J. (2020) IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI2021) [Paper] [Paper_IEEE] [arXiv] [cite:32]

  3. Causal Intervention for Weakly-Supervised Semantic Segmentation. Zhang, D., Zhang, H., Tang, J., Hua, X., & Sun, Q., Advances in Neural Information Processing Systems 33 (NeurIPS 2020) [Paper] [arXiv] [Code] [cite:14]

  4. Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect. Tang, K., Huang, J., & Zhang, H., Advances in Neural Information Processing Systems 33 (NeurIPS 2020) [Paper] [arXiv] [Code] [cite:4]

  5. Visual Commonsense Representation Learning via Causal Inference. Wang, T., Huang, J., Zhang, H., & Sun, Q. (2020). In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (pp. 378-379). [Paper] [Code] [cite:1]

  6. Causal Discovery with Reinforcement Learning. Zhu, S., Ng, I., & Chen, Z., In International Conference on Learning Representations. (ICLR2020) [Paper] [Review] [Code] [cite:29]

  7. DeVLBert: Learning Deconfounded Visio-Linguistic Representations. Zhang, S., Jiang, T., Wang, T., Kuang, K., Zhao, Z., Zhu, J., ... & Wu, F. (2020, October). In Proceedings of the 28th ACM International Conference on Multimedia. [Paper] [arXiv] [Code] [cite:2]

  8. Algorithmic Decision Making with Conditional Fairness. Xu, R., Cui, P., Kuang, K., Li, B., Zhou, L., Shen, Z., & Cui, W. (2020, August). In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. [Paper] [arXiv] [Code] [cite:0]

2019

  1. Causal reasoning from meta-reinforcement learning. Dasgupta, I., Wang, J., Chiappa, S., Mitrovic, J., Ortega, P., Raposo, D., ICLR2019 [Paper] [arXiv] [Code] [cite:42]

  2. Learning to Compose and Reason with Language Tree Structures for Visual Grounding. R. Hong, D. Liu, X. Mo, X. He and H. Zhang, In IEEE Transactions on Pattern Analysis and Machine Intelligence, doi: 10.1109/TPAMI.2019.2911066. [Paper] [arXiv] [cite:10]

  3. Counterfactual Critic Multi-Agent Training for Scene Graph Generation. Chen, L., Zhang, H., Xiao, J., He, X., Pu, S., & Chang, S. F., In Proceedings of the IEEE/CVF International Conference on Computer Vision (CVPR 2019). [Paper] [arXiv] [Video] [supp] [cite:39]

  4. A Graph Autoencoder Approach to Causal Structure Learning. Ng, I., Zhu, S., Chen, Z., & Fang, Z., Advances in Neural Information Processing Systems Workshop (NeurIPS 2019) [Paper] [arXiv] [Code] [cite:7]

  5. MetaCI: Meta-Learning for Causal Inference in a Heterogeneous Population. Sharma, A., Gupta, G., Prasad, R., Chatterjee, A., Vig, L., & Shroff, G., Advances in Neural Information Processing Systems Workshop (NeurIPS 2019) [Paper] [arXiv] [cite:4]

2018

  1. Causally regularized learning with agnostic data selection bias. Shen, Z., Cui, P., Kuang, K., Li, B. and Chen, P., 2018. Proceedings of the 26th ACM international conference on Multimedia. [Paper] [arXiv] [cite:14]

2017

  1. Discovering causal signals in images. Lopez-Paz, D., Nishihara, R., Chintala, S., Scholkopf, B. and Bottou, L., 2017. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. [Paper] [arXiv] [Code] [Video] [cite:81]

  2. Estimating individual treatment effect: generalization bounds and algorithms. Shalit, U., Johansson, F. D., & Sontag, D. (2017, July). In International Conference on Machine Learning (pp. 3076-3085). PMLR. [Paper] [Code] [cite:306]

2016

  1. Learning Representations for Counterfactual Inference. Johansson, F., Shalit, U., & Sontag, D. (2016, June). (pp. 3020-3029). PMLR. [Paper] [cite:310]

Books

I highly recommend the book written by Judea pearl

  • The Bok of Why

Video Reports

Actually, there are plenty of videos and courses on the internet, here are just some of them.

  • Recent Progress of Causality in Vision. Hanwang, Zhang from NTU (But it's in mandarin, so it's might hard for some friends) [Link]✔️

  • MIT OpenCourseWare in YouTube. [Link] ✔️

  • Causal Inference and Stable Learning from ICML2019. [Link] ✔️

Tutorial

  • The Effect: An Introduction to Research Design and Causality. [Link]

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