Chinese Version

Ruichu Cai   蔡瑞初 Second-Class Professor Lab of DMIR, Data mining and Information Retrieval Laboratory College of Computer Science and Technology Guangdong University of Technology Guangzhou, China. 510000. Email: cairuichu@gmail.com Mobile: +86-158-0003-0523

Biography

I am a Second-Class Professor and PhD supervisor at Guangdong University of Technology. I currently serve as Director of the Data mining and Information Retrieval Laboratory and am a recipient of the National Science Fund for Excellent Young Scholars. I obtained my Bachelor's degree in Applied Mathematics in 2005 and PhD degree in Computer Science in 2010, both from South China University of Technology. My research focuses on the theories and applications of causal discovery, causal learning and deep learning. I have led a number of key research projects, including the National Science Fund for Excellent Young Scholars, Major Projects of the Ministry of Science and Technology under the "Sci-Tech Innovation 2030" Plan, and the NSFC Regional Innovation and Development Joint Fund (Guangdong). I have been awarded the Natural Science Award of Guangdong and the China Patent Award. Currently, I serve as Associate Editor of IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), Action Editor of Neural Networks, and Youth Editor of Fundamental Research. I also act as an Area Chair for major conferences including NeurIPS, ICML and ICLR.

My research revolves around an unsolved fundamental problem in artificial intelligence. While most AI models can capture correlations between variables, they cannot answer how Y will change when X is altered and what drives such changes. This is essentially the problem of causality, which lies at the heart of reliable decision-making in real-world scenarios including network fault diagnosis, clinical decision-making and marketing pricing. Developing stable causal models for open and noisy real-world environments is a cutting-edge challenge in this field. As one of China’s earliest research groups dedicated to causal discovery and causal machine learning, the DMIR Laboratory aims to address a critical flaw of large models: enabling AI not only to perceive "what the world is", but also to understand "how the world will change as we take actions".

Education

• South China University of Technology Guangzhou, China

• Bachelor of Applied Mathematics and Ph.D. of Computer Science September 2001 - July 2010



• National University of Singapore Singapore

• Visiting Students of Computer Science September 2007 - September 2009

Experience

• Guangdong University of Technology

• Lecturer, Associate Professor, Full Professor, Second-Class Professor July 2010 - Present



• Advanced Digital Sciences Center, a center of University of Illinois at Singapore

• Visiting Senior Researcher July 2013 - July 2014



• Netease

• Data Scientist (Part-time consultant) November 2009 - June 2013

Research Interests

• Causal discovery and causality-related learning
• Deep learning, natural language processing and so on

News

• 2026/6/2, I has been invited to serve as Vice Chairman, Special Committee on Uncertainty Artificial Intelligence, Chinese Association for Artificial Intelligence (CAAI)!
• 20206/4/30, one paper 'On the Identifiability of Poisson Branching Structural Causal Model Under Latent Confounding' has been accepted as an Oral by ICML 2026!
• 20206/4/29, 2 paper of my group (DMIR) are acceted by IJCAI 2026!
• 2026/1/26, 2 papers of my group (DMIR) are accepted by ICLR 2026!
• 2025/12/9, one paper 'Time Series Domain Adaptation via Latent Invariant Causal Mechanism' has been accepted by TPAMI!
• 2025/11/10, one paper 'Horizontal and Vertical Federated Causal Structure Learning via Higher-order Cumulants' has been accepted at AAAAI 2026!
• 2025/11/1, I has been invited to serve as Associate Editor of IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI) !
• 2025/10/10, one paper 'Higher-order Cumulants based Method for Direct and Efficient Causal Discovery' has been accepted by TNNLS!
• 2025/10/9, one paper 'Temporal Recommendation Based on Adaptive Deep Matrix Factorization' has been accepted by IEEE Transactions on Big Data!
• 2025/8/31, one paper 'Identifying semantic component for robust molecular property prediction' has been accepted by TPAMI!
• 2025/5/2, 2 papers of my group (DMIR) are accepted by ICML 2025!
• 2025/4/28, 3 papers of my group (DMIR) are accepted by IJCAI 2025!
• 2025/4/24, one paper 'Learning by doing: an online causal reinforcement learning framework with causal-aware policy' has been accepted by SCIENCE CHINA Information Sciences!
• 2025/3/7, one paper 'Interpretable High-order Knowledge Graph Neural Network for Predicting Synthetic Lethality in Human Cancers' has been accepted by BBI!
• 2025/1/23, 2 papers of my group (DMIR) are aceepted by ICLR 2025!
• 2025/1/20,2 papers of my group (DMIR) are accepted by WWW 2025!
• 2024/12/16, 2 papers of my group (DMIR) are aceepted by Neural Networks!
• 2024/12/10, 2 papers of my group (DMIR) are aceepted by AAAI 2025!
• 2024/12/3, I have just secured funding for a key program from the National Natural Science Foundation of China!
• 2024/9/26, 2 papers of my group (DMIR) are aceepted by NeurIPS 2024!
• 2024/5/16, one paper 'S2GSL: Incorporating Segment to Syntactic Enhanced Graph Structure Learning for Aspect-based Sentiment Analysis' has been accepted by ACL 2024!
• 2024/5/4, one paper 'A survey on causal reinforcement learning' has been accepted by TNNLS!
• 2024/5/2, 4 papers of my group (DMIR) are aceepted by ICML 2024!
• 2024/4/23, one paper 'Long-term Causal Effects Estimation via Latent Surrogates Representation Learning' has been accepted by Neural Networks!
• 2024/4/20, I has been invited to serve as the youth editor of Fundamental Research!
• 2024/4/17, 2 papers have been accepted by IJCAI 2024!
• 2023/12/10, 8 papers of my group (DMIR) are accepted by AAAI 2024, congratulations!
• 2023/10/18, one paper 'On the role of entropy-based loss for learning causal structures with continuous optimization' has been accepted by TNNLS!
• 2023/10/03， I has been invited to serve as action editor of Neural Networks from 2024 to 2026!
• 2023/09/10， one paper 'REST: Debiased Social Recommendation via Reconstructing Exposure Strategies' is accepted by TKDD!
• 2023/08/06， one paper 'Transferable time-series forecasting under causal conditional shift' is accepted by TPAMI!

Selected Projects

• NSFC-Regional Innovation and Development Joint Fund (Guangdong), Causal Discovery and Game Decision Theory and Methodology for Optimization in Complex Manufacturing Processes, U24A20233, January 2025-December 2028
• National Key R&D Program of China, Research on Causal Inference and Decision Theories, 2021ZD0111500, January 2022-December 2025
• National Science Fund for Excellent Young Scholars, Causal Inference Theory and Methods for High-Dimensional Heterogeneous Data, 62122022, January 2022-December 2024
• National Natural Science Foundation of China, Research on Causal Mechanism and Methods of Nonstationary Social Network User Behaviors, 61876043, January 2019-December 2022
• National Natural Science Foundation of China, Research on Causal Mechanism and Methods of Nonstationary Social Network User Behaviors, 61876043
• Causal Discovery on Spatial-Temporal Data with Latent Confounders (October 2021-October 2022), supported by Huawei
• Causal Discovery on High Dimensional Alarm Data and Applications (September 2019- July 2021), supported by Huawei
• Long-term Causal-effect Analysis based on Short-term Surrogates (January 2022-December 2022), supported by Didi Chuxing

Industrial Collaboration

My team at DMIR Lab specializes in translating causal AI research into real-world business impact. We have maintained long-term partnerships with industry leaders across multiple sectors. I welcome new collaboration opportunities — feel free to reach me at cairuichu@gmail.com.

Huawei (2019–Present)

• Causal Discovery on High-Dimensional Alarm Data — Built causal graphs over 5GC network alarms to automatically identify root causes of cascading failures. Deployed in operational network environments. Led to 3 consecutive collaborations.
• Causal Discovery on Spatial-Temporal Data with Latent Confounders — Extended causal structure learning to scenarios with hidden confounders in telecom networks.

DiDi Chuxing (2021–Present)

• Causal Treatment Effect Estimation for Multi-Category Pricing — Replaced expensive RCTs with observational causal models for ride-hailing pricing optimization across 6 service categories.
• Long-Term Effect Estimation from Short-Term Surrogates — Causal transfer across time scales for policy evaluation. Led to 4 consecutive collaborations.

Vipshop (2017–2019)

• Causal User Profiling and Recommendation — Built causal attribution models for user behavior analysis and personalized cross-domain recommendations.

Selected Publications

Causal Discovery

• Wei Chen; Linjun Peng; Zhiyi Huang; Ruichu Cai*; Zhifeng Hao; Kun Zhang*. Higher Order Cumulants-Based Method for Direct and Efficient Causal Discovery. TNNLS 2025
• Wei Chen, Wanyang Gu, Linjun Peng, Ting Yan, Ruichu Cai, Zhifeng Hao, Kun Zhang. Horizontal and Vertical Federated Causal Structure Learning via Higher-order Cumulants. AAAI 2026
• Ruichu Cai,, Xiaokai Huang, Wei Chen*, Zijian Li, Zhifeng Hao. Temporal latent variable structural causal model for causal discovery under external interferences. Neurocomputing 2025
• Yuequn Liu, Guangdong Sun, Ruichu Cai*, Zijian Li, Keli Zhang, Lujia Pan, Zhifeng Hao. StateHPs: State Hawkes processes for Granger causal discovery from non-stationary event sequences. Information Sciences 2025
• Zijian Li, Yifan Shen, Kaitao Zheng, Ruichu Cai*, Xiangchen Song, Mingming Gong, Guangyi Chen, Kun Zhang. On the Identification of Temporal Causal Representation with Instantaneous Dependence. ICLR 2025
• Yuan Fang, Xiaofeng Feng, Geping Yang, Ruichu Cai, Yiyang Yang*, Zhiguo Gong*, Zhifeng Hao. EVA-MVC: Equitable View-weight Allocation for Generic Multi-View Clustering. WWW 2025
• Yu Xiang, Jie Qiao, Zhefeng Liang, Zihuai Zeng, Ruichu Cai*, Zhifeng Hao. On the Identifiability of Poisson Branching Structural Causal Model Using Probability. NeurIPS 2024
• Zhengming Chen, Ruichu Cai*, Feng Xie, Jie Qiao,Anpeng Wu, Zijian Li, Zhifeng Hao, Kun Zhang* Learning Discrete Latent Variable Structures with Tensor Rank Conditions. NeurIPS 2024
• Wei Chen, Xiaokai Huang, Zijian Li, Ruichu Cai*, Zhiyi Huang, Zhifeng Hao. Individual Causal Structure Learning from Population Data. IJCAI 2024
• Wei Chen, Zhiyi Huang, Ruichu Cai*, Zhifeng Hao, Kun Zhang. Identification of Causal Structure with Latent Variables based on Higher Order Cumulants. AAAI 2024
• Yuequn Liu, Ruichu Cai*, Wei Chen, Jie Qiao, Yuguang Yan, Zijian Li, Keli Zhang, Zhifeng Hao. TNPAR: Topological Neural Poisson Auto-Regressive Model for Learning Granger Causal Structure from Event Sequences. AAAI 2024
• Jie Qiao, Zhengming Chen, Jianhua Yu, Ruichu Cai*, Zhifeng Hao. Identification of Causal Structure in the Presence of Missing Data with Additive Noise Model. AAAI 2024
• Jie Qiao, Yu Xiang, Zhengming Chen, Ruichu Cai*, Zhifeng Hao. Causal Discovery from Poisson Branching Structural Causal Model Using High-Order Cumulant with Path Analysis. AAAI 2024
• Weilin Chen, Jie Qiao, Ruichu Cai*, Zhifeng Hao. On the role of entropy-based loss for learning causal structures with continuous optimization. TNNLS 2023
• Ruichu Cai, Zhiyi Huang, Wei Chen, Zhifeng Hao, Kun Zhang. Causal Discovery with Latent Confounders Based on Higher-Order Cumulants. ICML 2023
• Jie Qiao, Ruichu Cai*, Kun Zhang, Zhenjie Zhang, Zhifeng Hao. Causal Discovery with Confounding Cascade Nonlinear Additive Noise Models. ACM Transactions on Intelligent Systems and Technology (TIST), 2021: 12(6): 1-28
• Wei Chen, Ruichu Cai*, Kun Zhang, Zhifeng Hao. Causal Discovery in Linear Non-Gaussian Acyclic Model with Multiple Latent Confounders. IEEE Transactions on Neural Networks and Learning Systems, 2021
• 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
• Feng Xie, Ruichu Cai*, Yan Zeng, Jiantao Gao, Zhifeng Hao. An Efficient Entropy-Based Causal Discovery Method for Linear Structural Equation Models with IID Noise Variables. IEEE Transactions on Neural Networks and Learning Systems, 2020, 31(5): 1667 - 1680
• Ruichu Cai, Jincheng Ye, Jie Qiao, Huiyuan Fu, Zhifeng Hao. FOM: Fourth-Order Moment based Causal Direction Identification on the Heteroscedastic Data. Neural Networks, 2020, 124:193-201
• Ruichu Cai, Feng Xie, Clark Glymour, Zhifeng Hao, Kun Zhang. Triad Constraints for Learning Causal Structure of Latent Variables. NeurIPS 2019
• Ruichu Cai, Jie Qiao, Kun Zhang, Zhenjie Zhang, Zhifeng Hao. Causal Discovery with Cascade Nonlinear Additive Noise Model. IJCAI 2019
• 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, 2018:29(8) : 3623 - 3635
• Ruichu Cai, Jie Qiao, Zhenjie Zhang , et al. SELF: Structural Equational Likelihood Framework for Causal Discovery. AAAI 2018
• Ruichu Cai, Jie Qiao, Kun Zhang, Zhenjie Zhang, Zhifeng Hao. Causal Inference from Discrete Data using Hidden Compact Representation. NeurIPS 2018
• Ruichu Cai, Zhenjie Zhang, Zhifeng Hao. SADA: A General Framework to Support Robust Causation Discovery. ICML 2013

• Jie Qiao, Ruichu Cai, Siyu Wu, Yu Xiang, Kun Zhang, Zhifeng Hao. Structural Hawkes Processes for Learning Causal Structure from Discrete-Time Event. IJCAI 2023
• Ruichu Cai, Siyu Wu, Jie Qiao, Zhifeng Hao, Keli Zhang, Xi Zhang. THPs: Topological Hawkes Processes for Learning Causal Structure on Event Sequences. IEEE Transactions on Neural Networks and Learning Systems. 2022, 35(1):479 - 493
• 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

Causality-Related Learning

• Yuguang Yan, Haolin Yang, Shihao Zhang, Weilin Chen, Ruichu Cai*, Zhifeng Hao. Matching without Group Barrier for Heterogeneous Treatment Effect Estimation. ICLR 2026
• Ruichu Cai*, Xi Chen, Jie Qiao, Zijian Li, Yuequn Liu, Wei Chen, Keli Zhang, Jiale Zheng. An identifiable cost-aware causal decision-making framework using counterfactual reasoning. Neural Networks 2026
• Ruichu Cai, Junxian Huang, Zhenhui Yang, Zijian Li, Emadeldeen Eldele, Min Wu, Fuchun Sun.Time Series Domain Adaptation via Latent Invariant Causal Mechanism. TPAMI 2025
• Wei Chen, Jiahao Zhang, Haipeng Zhu, Boyan Xu, Zhifeng Hao, Keli Zhang, Junjian Ye, Ruichu Cai*. Causal-aware Large Language Models: Enhancing Decision-Making through Learning, Adapting and Acting. IJCAI 2025
• Zijian Li, Zunhong Xu, Ruichu Cai*, Zhenhui Yang, Yuguang Yan, Zhifeng Hao, Guangyi Chen, Kun Zhang. Identifying semantic component for robust molecular property prediction. IEEE Transactions on Pattern Analysis and Machine Intelligence 2025
• Ruichu Cai*, Siyang Huang, Jie Qiao, Wei Chen, Yan Zeng, Keli Zhang, Fuchun Sun, Yang Yu, Zhifeng Hao. Learning by doing: an online causal reinforcement learning framework with causal-aware policy. SCIENCE CHINA Information Sciences 2025
• Ruichu Cai, Zhifan Jiang, Kaitao Zheng, Zijian Li*, Weilin Chen, Xuexin Chen, Yifan Shen, Guangyi Chen, Zhifeng Hao, Kun Zhang. Learning Disentangled Representation for Multi-Modal Time-Series Sensing Signals. WWW 2025
• Zijian Li, Shunxing Fan, Yujia Zheng, Ignavier Ng, Shaoan Xie, Guangyi Chen, Xinshuai Dong, Ruichu Cai, Kun Zhang. Synergy Between Sufficient Changes and Sparse Mixing Procedure for Disentangled Representation Learning. ICLR 2025
• Xuexin Chen, Ruichu Cai*, Kaitao Zheng, Zhifan Jiang, Zhengting Huang, Zhifeng Hao, Zijian Li. Unifying invariant and variant features for graph out-of-distribution via probability of necessity and sufficiency. Neural Networks 2025
• Ruichu Cai, Haiqin Huang, Zhifan Jiang, Changze Zhou, Yuequn Liu, Yuming Liu, Zhifeng Hao, Zijian Li. Disentangling Long-Short Term State Under Unknown Interventions for Online Time Series Forecasting. AAAI 2025
• Yan Zeng, Ruichu Cai, Fuchun Sun, Libo Huang, Zhifeng Hao. A survey on causal reinforcement learning. TNNLS 2024
• Xuexin Chen, Ruichu Cai*, Zhengting Huang, Yuxuan Zhu, Julien Horwood, Zhifeng Hao, Zijian Li, Jose Miguel Hernandez-Lobato. Feature Attribution with Necessity and Sufficiency via Dual-stage Perturbation Test for Causal Explanation.ICML 2023
• Ruichu Cai, Yuxuan Zhu, Jie Qiao, Zefeng Liang, Furui Liu, Zhifeng Hao. Where and How to Attack? A Causality-Inspired Recipe for Generating Counterfactual Adversarial Examples. AAAI 2024
• Ruichu Cai*, Fengzhu Wu, Zijian Li, Jie Qiao, Wei Chen, Yuexing Hao, Hao Gu. REST: Debiased Social Recommendation via Reconstructing Exposure Strategies. TKDD 2023
• Zijian Li, Ruichu Cai*, Tom Fu, Zhifeng Hao, Kun Zhang. Transferable time-series forecasting under causal conditional shift. TPAMI 2023
• 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
• 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
• Ruichu Cai, Zijian Li, Pengfei Wei, Jie Qiao, Kun Zhang, Zhifeng Hao. Learning Disentangled Semantic Representation for Domain Adaptation. IJCAI 2019

Deep Learning

• Ruichu Cai, Kaitao Zheng, Junxian Huang, Zijian Li, Zhengming Chen, Boyan Xu, Zhifeng Hao. Causal View of Time Series lmputation: Some ldentification Results on Missing Mechanism. IJCAI 2025
• Xuexin Chen, Ruichu Cai*, Zhengting Huang, Zijian Li, Jie Zheng, Min Wu*. Interpretable High-order Knowledge Graph Neural Network for Predicting Synthetic Lethality in Human Cancers. Briefings in Bioinformatics
• Boyan Xu, Yuyuan Cai, Shaobin Shi, Zhifeng Hao, Ruichu Cai*. Chat2DB: Chatting to the Database with Interactive Agent Assisted Language Models. ICDE 2025
• Ruichu Cai, Junhao Lu, Zhongjie Chen, Boyan Xu*, Zhifeng Hao. Handling Missing Entities in Zero-Shot Named Entity Recognition: Integrated Recall and Retrieval Augmentation. NAACL 2025
• Bingfeng chen, Shaobin Shi, yongqi luo, Boyan Xu*, Ruichu Cai, Zhifeng Hao. Track-SQL: Enhancing Generative Language Models with Dual-Extractive Modules for Schema and Context Tracking in Multi-turn Text-to-SQL. NAACL 2025
• Bingfeng chen, Chenjie Qiu, Yifeng Xie, Boyan Xu*, Ruichu Cai, Zhifeng Hao. $S^2$IT: Stepwise Syntax Integration Tuning for Large Language Models in Aspect Sentiment Quad Prediction. NAACL 2025
• Ruichu Cai , Shengyin Yu , Jiahao Zhang , Wei Chen , Boyan Xu, Keli Zhang. Dr.ECI: Infusing Large Language Models with Causal Knowledge for Decomposed Reasoning in Event Causality Identification. Coling 2025
• Bingfeng Chen, Haoran Xu, Yongqi Luo, Boyan Xu, Ruichu Cai, Zhifeng Hao. CACA: Context-Aware Cross-Attention Network for Extractive Aspect Sentiment Quad Prediction. Coling 2025
• Yuguang Yan, Canlin Yang, Yuanlin Chen, Ruichu Cai*, Michael Ng. Hypergraph Learning for Unsupervised Graph Alignment via Optimal Transport. AAAI 2025
• Jian Zhu，Shuliu Wu，Yutang Xiao*, Boyu Wang，Ruichu Cai. Dual-contrastive Multi-view Graph Attention Network for Industrial Fault Diagnosis under Domain and Label Shift. IEEE Transactions on Instrumentation & Measurement
• Bingfeng Chen, Qihan Ouyang, Yongqi Luo, Boyan Xu*, Ruichu Cai, Zhifeng Hao. S2GSL: Incorporating Segment to Syntactic Enhanced Graph Structure Learning for Aspect-based Sentiment Analysis. ACL 2024
• Jiahao Li, Ruichu Cai*, Yuguang Yan. Combinatorial Routing for Neural Trees. IJCAI 2024
• Yuguang Yan, Zhihao Xu, Canlin Yang, Jie Zhang, Ruichu Cai*, Michael Kwok-Po Ng. An Optimal Transport View for Subspace Clustering and Spectral Clustering. AAAI 2024
• Yuguang Yan, Yuanlin Chen, Shibo Wang, Hanrui Wu, Ruichu Cai*. Hypergraph Joint Representation Learning for Hypervertices and Hyperedges via Cross Expansion. AAAI 2024
• Xuexin Chen, Ruichu Cai*, Yuan Fang, Min Wu, Zijian Li, Zhifeng Hao. Motif Graph Neural Network. IEEE Transactions on Neural Networks and Learning Systems. 2023, Early Access
• Zhifeng Hao, Junbin Chen, Wen Wen, Biao Wu, Ruichu Cai. A selection-pattern-aware recommendation model with colored-motif attention network. Neurocomputing, 2023: 538: 126-178
• Ruichu Cai, Jinjie Yuan, Boyan Xu, Zhifeng Hao. SADGA: Structure-Aware Dual Graph Aggregation Network for Text-to-SQL. NeurIPS 2021
• Ruichu Cai, Hao Zhang, Wen Liu, Shenghua Gao, Zhifeng Hao. Appearance-Motion Memory Consistency Network for Video Anomaly Detection. AAAI 2021
• Ruichu Cai, Zhihao Liang, Boyan Xu, zijian li, Yao Chen and Yuexing Hao. TAG: Type Auxiliary Guiding for Code Comment Generation. ACL 2020
• Ruichu Cai, Xuexin Chen, Yuan Fang, Min Wu, Yuexing Hao. Dual-Dropout Graph Convolutional Network for Predicting Synthetic Lethality in Human Cancers. Bioinformatics, 2020, 36(16):4458-4465
• Ruichu Cai, Boyan Xu, Xiaoyan Yang, Zhengjie Zhang, Zijian Li, Zhihao Liang. An Encoder-Decoder Framework Translating Natural Language to Database Queries. IJCAI 2018

Causal Effect

• Ruichu Cai*, junjie Wan, Weilin Chen, Zegin Yang, Ziian Li, Pena Zhen, Jiecheng Guo.Long-Term Individual Causal Effect Estimation via ldentifiable Latent Representation Learning.IJCAI 2025
• Yuguang Yan, Hao Zhou, Zeqin Yang, Weilin Chen, Ruichu Cai*, Zhifeng Hao. Reducing Balancing Error for Causal Inference via Optimal Transport. ICML 2024
• Feng Xie, Zhengming Chen, Shanshan Luo, Wang Miao, Ruichu Cai, Zhi Geng. Automating the Selection of Proxy Variables of Unmeasured Confounders. ICML 2024
• Weilin Chen, Ruichu Cai*, Zeqin Yang, Jie Qiao, Yuguang Yan, Zijian Li, Zhifeng Hao. Doubly Robust Causal Effect Estimation under Networked Interference via Targeted Learning. ICML 2024
• Ruichu Cai*, Weilin Chen, Zeqin Yang, Shu Wan, Chen Zheng, Xiaoqing Yang, Jiecheng Guo. Long-term Causal Effects Estimation via Latent Surrogates Representation Learning. Neural Networks, 2024
• Yuguang Yan, Zeqin Yang, Weilin Chen, Ruichu Cai*, Zhifeng Hao, Michael Kwok-Po Ng. Exploiting Geometry for Treatment Effect Estimation via Optimal Transport. AAAI 2024
• Ruichu Cai, Zeqin Yang, Weilin Chen, Yuguang Yan, Zhifeng Hao. Generalization Bound for Estimating Causal Effects from Observational Network Data. CIKM 2023

2019 and prior

• Ruichu Cai, Zijie Lu, Li Wang, Zhenjie Zhang. DITIR: Distributed Index for High Throughput Trajectory Insertion and Real-time Temporal Range Query. PVLDB 2017
• 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
• Ruichu Cai, Zhenjie Zhang, Srinivasan Parthasarathy, Anthony K. H. Tung, Zhifeng Hao, Wen Zhang. Multi-Domain Manifold Learning for Drug-Target Interaction Prediction. SDM 2016
• Ruichu Cai, Zhifeng Hao, Marianne Winslett, Xiaokui Xiao, Yang Yin, Zhenjie Zhang, Shuigeng Zhou. Deterministic Identification of Specific Individuals from GWAS Results[J]. Bioinformatics, 2015, 31(11): 1701-1707
• Yong Hu, Xiangzhou Zhang, EWT Ngai, Ruichu Cai, Mei Liu. Software project risk analysis using Bayesian networks with causality constraints. Decision Support Systems, 2013, 56: 439-449
• Ruichu Cai, Zhenjie Zhang, Zhifeng Hao. Causal Gene Identification Using Combinatorial V-Structure Search, Neural Networks. 2013;43:63-71
• Mei Liu, Ruichu Cai (Co-First Author), Yong Hu, ME Matheny, Jianchuan Sun, Jun Hu. Determining molecular predictors of adverse drug reactions with causality analysis based on structure learning[J]. JAMIA, 2013, 21(2):245-51
• Ruichu Cai, Tung K.H. Anthony, Zhifeng Hao, Zhenjie Zhang. What is Unequal among the Equals? Ranking Equivalent Rules from Gene Expression Data. IEEE Transactions on Knowledge and Data Engineering, 2011;23(11):1735-1747

Honors and Awards

• National Science Fund for Excellent Young Scholars - January, 2022
• The China Patent Awards Excellence Award - October, 2019
• 1st Class, Natural Science Award of Guangdong - April, 2016
• National Science Fund for Excellent Young Scholars of Guangdong - May, 2014
• 2nd Class, Natural Science Award of Guangdong - April, 2014

Services

• Associate Editor: IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI)
• Action Editor: Neural Networks
• The Youth Editor: Fundamental Research
• Area Chair: ICML2022, ICML2023, ICML2024, NeurIPS2022, NeurIPS2023, NeurIPS2024, ICLR2024, UAI2021, UAI2022, UAI2023
• Senior PC: AAAI 2019-2022, IJCAI 2019-2021
• PC: AAAI 2015-2019, IJCAI 2018-2019, NIPS 2016-2018, ICML 2015-2018, AISTATS 2016-2019, ICLR 2018-2019
• Associate Editor: Frontiers in Bioinformatics
• Reviewer: TNNLS, TPAMI, TKDE, TIST, Neural Network, Pattern Recognition, Bioinformatics, Neurocomputing, Information Sciences, National Science Review, Science China-Information Sciences and so on.

Useful Links

Portfolio: https://sites.google.com/site/cairuichu

My Group (in alphabetical order): Wei Chen , Jie Qiao, Wen Wen, Boyan Xu , Yuguang Yan ...

Github: https://github.com/DMIRLAB-Group

Causal Learn: causal-learn: Causal Discovery in Python