Ruichu Cai 蔡瑞初

二级教授

数据挖掘与信息检索实验室
计算机学院
广东工业大学
中国, 广州. 510000.

邮箱: cairuichu@gmail.com

个人简介

我是广东工业大学博士生导师、二级教授，现任数据挖掘与信息检索实验室主任，国家优秀青年科学基金获得者。我分别于2005年和2010年获得华南理工大学应用数学学士学位和计算机科学博士学位。我的研究主要围绕因果发现、因果学习与深度学习的理论及应用展开。我主持了国家优秀青年科学基金、科技部 “科技创新 2030” 重大项目、国家自然科学基金 - 区域创新发展联合基金（广东）等多项重点课题。我曾获得广东省自然科学奖、中国专利奖等奖项。现担任 IEEE TPAMI 副主编、Neural Networks 执行编委、Fundamental Research 青年编委，并担任 NeurIPS、ICML、ICLR 等会议的领域主席（Area Chair）。

我的研究围绕人工智能底层一个尚未解决的问题：大多数 AI 模型能揭示变量间的相关性，却无法回答 “改变 X 后 Y 会如何变化，以及背后的原因”—— 这正是因果问题，也是网络故障诊断、临床决策、营销定价等真实场景中一切可靠决策的核心。在开放、嘈杂的真实世界中实现稳定的因果建模，是当前领域的前沿挑战。DMIR 实验室是国内最早专注于因果发现与因果机器学习的团队之一，我们致力于补上大模型的关键短板：不只是认识“世界是什么”，而是理解“如果我们行动，世界会变成什么样”。

教育经历

华南理工大学
中国, 广州
应用数学学士和计算机科学博士学位 2001 年 9 月 - 2010 年 7 月

新加坡国立大学
新加坡
计算机应用技术(联合培养)工学博士学位 2007 年 9 月 - 2009年 9 月

工作经历

广东工业大学
讲师，副教授，正教授，二级教授 2010 年 7 月 - 现在

Advanced Digital Sciences Center, a center of University of Illinois at Singapore
客座高级研究员 2013 年 7 月 - 2014 年 7 月

网易
数据科学家(兼职顾问) 2009 年 11 月 - 2013 年 6 月

研究兴趣

因果关系发现与因果性学习
深度学习，自然语言处理等等

News

2026/10/10，论文 “Towards precision medicine: Application of the structural causal model in psychiatry” 已发表于 General Psychiatry！
2026/10/10，论文 “Causal Effect Estimation under Networked Interference without Networked Unconfoundedness Assumption” 被 TPAMI 录用！
2026/9/30，我们发布了TSC-FM Benchmark：一个面向时间序列分类基础模型的统一评测平台。该平台涵盖 198 个数据集，提供标准与少样本排行榜，并通过能力图谱揭示模型在整体准确率之外的优势与局限。
2026/7/13, 我们发布了CDFM—— 一款面向零样本因果发现任务的预训练基础模型！
2026/6/2, 应邀担任中国人工智能学会不确定性人工智能专委会副主任委员！
2026/4/30, 1篇文章 “On the Identifiability of Poisson Branching Structural Causal Model Under Latent Confounding [code]” 被 ICML 2026 录用！
2026/4/29, 团队（DMIR实验室）的2篇论文被 IJCAI 2026 录用！
2026/2/22, 1篇文章 “Hierarchical Action Learning for Weakly-Supervised Action Segmentation” 被CVPR 2026 录用！
2026/1/26, 团队（DMIR实验室）的2篇论文被 ICLR 2026 录用！
2025/12/9, 1篇文章 “Time Series Domain Adaptation via Latent Invariant Causal Mechanism” 被 TPAMI 录用！
2025/11/10, 1篇文章 “Horizontal and Vertical Federated Causal Structure Learning via Higher-order Cumulants” 被 AAAAI 2026 录用!
2025/11/1，应邀担任 IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI) 杂志的副主编！
2025/10/10, 1篇文章 “Higher-order Cumulants based Method for Direct and Efficient Causal Discovery” 被 TNNLS 录用！
2025/10/9, 1篇文章 “Temporal Recommendation Based on Adaptive Deep Matrix Factorization” 被 IEEE Transactions on Big Data 录用！
2025/8/31, 1篇文章 “Identifying semantic component for robust molecular property prediction” 被 TPAMI 录用！
2025/5/2, 团队（DMIR实验室）的2篇论文被 ICML 2025 录用！
2025/4/28, 团队（DMIR实验室）的3篇论文被 IJCAI 2025 录用！
2025/4/24, 1篇文章 “Learning by doing: an online causal reinforcement learning framework with causal-aware policy” 被 SCIENCE CHINA Information Sciences 录用！
2025/3/7, 1篇文章 “Interpretable High-order Knowledge Graph Neural Network for Predicting Synthetic Lethality in Human Cancers” 被 BIB 录用！
2025/1/23，团队（DMIR实验室）的2篇论文被 ICLR 2025 录用！
2025/1/20，团队（DMIR实验室）的2篇论文被 WWW 2025 录用！
2024/12/16，团队（DMIR实验室）的2篇论文被 Neural Networks 录用！
2024/12/10，团队（DMIR实验室）的2篇论文被 AAAI 2025 录用！
2024/12/3，新获批NSFC-区域创新发展联合基金（广东）项目1项！
2024/9/26，团队(DMIR实验室)的2篇论文被 NeurIPS 2024 录用！
2024/5/16, 1篇文章“S2GSL: Incorporating Segment to Syntactic Enhanced Graph Structure Learning for Aspect-based Sentiment Analysis”被 ACL 2024 录用！
2024/5/4， 1篇文章"A survey on causal reinforcement learning"被 TNNLS 录用！
2024/5/2，团队(DMIR实验室)的4篇论文被 ICML 2024 录用！
2024/4/23，1篇文章"Long-term Causal Effects Estimation via Latent Surrogates Representation Learning"被 Neural Networks 录用！
2024/4/20，应邀担任 Fundamental Research 杂志的青年编委！
2024/4/17，团队(DMIR实验室)的2篇文章被 IJCAI 2024 录用！
2023/12/10，团队(DMIR实验室)的8篇论文被AAAI 2024录用！
2023/10/19， 1篇文章"On the role of entropy-based loss for learning causal structures with continuous optimization"被 TNNLS 录用！
2023/10/11，应邀担任《计算机工程》青年编委！
2023/10/03，应邀担任 Neural Networks 杂志的执行编辑（Action Editor）！
2023/09/10， 1篇文章"REST: Debiased Social Recommendation via Reconstructing Exposure Strategies"被TKDD录用！
2023/08/06， 1篇文章"Transferable time-series forecasting under causal conditional shift"被TPAMI录用！

科研项目

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

代表性开源项目

CDFM：面向零样本因果发现任务的预训练基础模型。
TSC-FM Benchmark：面向时间序列分类基础模型的统一评测平台，涵盖198个数据集，并提供标准与少样本排行榜及能力图谱。
causal-learn：Python 因果发现开源工具库（社区项目）。

产业合作

DMIR实验室将因果AI研究应用于产业实际问题。合作介绍仅概述研究方向，不公开具体项目的技术细节与业务信息。欢迎合作交流，联系邮箱：cairuichu@gmail.com。

华为

因果发现、根因定位等在通信网络分析与故障诊断等方面的应用。

滴滴出行

因果推断、因果基础模型等在营销定价、策略评估等方面的应用。

唯品会

因果推荐在用户行为分析等方面的应用。

南方传媒集团

因果推理在大模型幻觉治理等方面的应用。

主要论文

因果关系发现

Jie Qiao, Zihuai Zeng, Zhengming Chen, Ruichu Cai*, Zhifeng Hao. On the Identifiability of Poisson Branching Structural Causal Model Under Latent Confounding [code]. ICML 2026 (Oral)
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 [code]. 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 Generating Function [code]. 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 [code]. AAAI 2024
Weilin Chen, Jie Qiao, Ruichu Cai*, Zhifeng Hao. On the role of entropy-based loss for learning causal structures with continuous optimization [code]. 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 [code]. 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 [code]. AAAI 2018
Ruichu Cai, Jie Qiao, Kun Zhang, Zhenjie Zhang, Zhifeng Hao. Causal Discovery 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 [code]. 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

因果性学习

Wei Chen, Liya Sun, Fanyuan Zhang, Jijun Wang, Chunbo Li, Shengying Qin, Chunling Wan, Fangyu Chen, Chen Zhang, Ruichu Cai. Towards precision medicine: Application of the structural causal model in psychiatry. General Psychiatry 2026, 39(5): e70053
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
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 [code]. 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 [code]. 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 [code]. 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 [code].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 [code]. 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 [code]. IJCAI 2019

深度学习

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 [code]. 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 [code]. 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 [code]. 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 [code]. 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 [code]. 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 [code]. 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 [code]. 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 [code]. 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 [code]. 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

因果效应

Weilin Chen, Ruichu Cai, Jie Qiao, Yuguang Yan, José Miguel Hernández-Lobato. Causal Effect Estimation under Networked Interference without Networked Unconfoundedness Assumption [code]. TPAMI 2026 (accepted)
Yuguang Yan, Haolin Yang, Zecong Chen, Weilin Chen, Ruichu Cai*, Zhifeng Hao. Adjusting Prediction Model Through Wasserstein Geodesic for Causal Inference. ICLR 2026
Yuguang Yan, Hao Zhou, Zeqin Yang, Weilin Chen, Ruichu Cai*, Zhifeng Hao. Reducing Balancing Error for Causal Inference via Optimal Transport [code]. 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 [code]. 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 [code]. 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 [code]. CIKM 2023

2019年及之前

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

主要荣誉

国家自然科学基金优秀青年科学基金获得者 - 2022 年 1 月
中国专利优秀奖 - 2019 年 10 月
广东省自然科学一等奖 - 2016 年 4 月
广东省自然科学基金优秀青年科学基金获得者 - 2014 年 5 月
广东省自然科学二等奖 - 2014 年 4 月

学术兼职

Associate Editor: IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI)
Action Editor: Transactions on Machine Learning Research (TMLR)
Action Editor: Neural Networks
Vice Chair: Special Committee on Uncertainty Artificial Intelligence, Chinese Association for Artificial Intelligence (CAAI)
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.

实用链接

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

TSC-FM Benchmark: https://tsc-fm.dmirlab.com/

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

我的团队 (拼音首字母排序): 陈薇 , 乔杰, 温雯, 许柏炎 , 闫玉光 ...

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

Causal Learn: causal-learn: Causal Discovery in Python