CDMIR (Causal Discovering, ModelIng and Reasoning) is a repository for providing causality papers from DMIR Lab, GDUT. These papers are related works on causal discovery from observational data and its application, especially with latent variables, non-iid. data and so on.

• 1. Causal Discovery
  • 1.1 Without Latent Variable
  • 1.2 With Latent Variable
  • 1.3 With Non-iid Data
• 2. Causality-Related Learning
• 3. Application of Causal Discovery

1. Ruichu Cai, Zhenjie Zhang, Zhifeng Hao. SADA: A General Framework to Support Robust Causation Discovery, ICML 2013 [pdf].
2. Mei Liu, Ruichu Cai (co-first author), Yong Hu, Michael E Matheny, Jingchun Sun, Jun Hu, Hua Xu. Determining molecular predictors of adverse drug reactions with causality analysis based on structure learning, Journal of the American Medical Informatics Association, 2013 [pdf]
3. Ruichu Cai, Zhenjie Zhang, Zhifeng Hao. Causal Gene Identification Using Combinatorial V-Structure Search, Neural Networks. 2013;43:63-71[pdf].
4. Ruichu cai, Zhenjie Zhang, Zhifeng Hao, Marianne Winslett. Sophisticated Merging over Random Partitions: A Scalable and Robust Causal Discovery Approach. IEEE Transactions on Neural Networks and Learning Systems,2017
5. Ruichu Cai, Mei Liu, Yong Hu , Brittany L. Melton, Michael E. Matheny,Hua Xu, Lian Duan, Lemuel R. Waitman. Identification of adverse drug-drug interactions through causal association rule discovery from spontaneous adverse event reports. Artificial Intelligence in Medicine 76 (2017) 7–15[code]
6. Ruichu Cai, Jie Qiao, Kun Zhang, Zhenjie Zhang, Zhifeng Hao. Causal Discovery on Discrete Data using Hidden Compact Representation. NIPS,2018. [pdf] [code]
7. Ruichu Cai, Jie Qiao, Zhenjie Zhang, Zhifeng Hao. SELF: Structural Equational Embedded Likelihood Framework for Causal Discovery. AAAI,2018. [pdf] [code]
8. Ruichu Cai, Jie Qiao, Kun Zhang, Zhenjie Zhang, Zhifeng Hao. Causal Discovery with Cascade Nonlinear Additive Noise Model. IJCAI 2019 [pdf] [code]

1. Ruichu Cai, Feng Xie, Clark Glymour, Zhifeng Hao, Kun Zhang. Triad Constraints for Learning Causal Structure of Latent Variables. NeurIPS 2019 [pdf] [code]
2. Feng Xie#, Ruichu Cai#, Biwei Huang, Clark Glymour, Zhifeng Hao, Kun Zhang#. Generalized Independent Noise Condition for Estimating Linear Non-Gaussian Latent Variable Graphs. NeurIPS 2020 [pdf] [code]
3. Chen, W., Cai, R., Zhang, K., & Hao, Z. (2021). Causal Discovery in Linear Non-Gaussian Acyclic Model With Multiple Latent Confounders. IEEE Transactions on Neural Networks and Learning Systems. [pdf]

1. Zeng, Y., Shimizu, S., Cai, R., Xie, F., Yamamoto, M., & Hao, Z. (2020). Causal discovery with multi-domain LiNGAM for latent factors. arXiv preprint arXiv:2009.09176. [pdf]
2. Cai, R., Wu, S., Qiao, J., Hao, Z., Zhang, K., & Zhang, X. (2021). THP: Topological Hawkes Processes for Learning Granger Causality on Event Sequences. arXiv preprint arXiv:2105.10884. [pdf]

1. Ruichu Cai, Zijian Li, Pengfei Wei, Jie Qiao, Kun Zhang, Zhifeng Hao. Learning Disentangled Semantic Representation for Domain Adaptation. IJCAI 2019 [pdf] [code]
2. Ruichu Cai, Jiahao Li, Zhenjie Zhang, Xiaoyan Yang, Zhifeng Hao. DACH: Domain Adaptation without Domain Information. IEEE Transactions on Neural Networks and Learning Systems, 2020:31(12):5055-5067 [pdf]
3. Ruichu Cai, Jiawei Chen, Zijian Li, Wen Chen, Keli Zhang, Junjian Ye, Zhuozhang Li, Xiaoyan Yang, Zhenjie Zhang. Time Series Domain Adaptation via Sparse Associative Structure Alignment, AAAI 2021 [pdf] [code]
4. Zijian Li, Ruichu Cai*, Hongwei Ng, Marianne Winslett, Tom Z. J. Fu, Boyan Xu, Xiaoyan Yang, Zhenjie Zhang. Causal Mechanism Transfer Network for Time Series Domain Adaptation in Mechanical Systems[J]. ACM Transactions on Intelligent Systems and Technology, 2021

1. Ruichu Cai, Zhenjie Zhang, Zhifeng Hao. BASSUM:A Bayesian semi-supervised method for classification feature selection,Pattern Recognition. 2011;44(4):811-820(SCI:711BV)
2. Ruichu Cai, Zhenjie Zhang, Zhifeng Hao, Marianne Winslett. Understanding Social Causalities Behind Human Action Sequences. IEEE Transactions on Neural Networks and Learning Systems. 2017,28(8):1801-1813.