Tian-Zuo Wang @ LAMDA, NJU-AI

王 天 佐 Tian-Zuo Wang (T.-Z. Wang) Assistant researcher, LAMDA Group School of Artificial Intelligence National Key Laboratory for Novel Software Technology Nanjing University, Nanjing 210023, China Email: wangtz@lamda.nju.edu.cn, wangtz@nju.edu.cn Laboratory: Computer Science Building, Xianlin Campus of Nanjing University

About Me

Currently I am an assistant researcher (Yuxiu Young Scholar) in School of Artificial Intelligence at Nanjing University, and I am also a member of LAMDA Group. I obtained my Ph.D. degree from Department of Computer Science and Technology in Nanjing University in September 2023, where I was very fortunate to be advised by Prof. Zhi-Hua Zhou.

致2025年报名保研的同学: 欢迎对因果学习、决策方法、因果大模型等研究方向感兴趣的同学报名。可与我通过邮件联系，并附上个人简历、成绩单、以及研究兴趣说明。

For prospective students: I mainly work on causal inference and decision-making methods leveraging structural information. Additionally, I am interested in applying causality to traditional machine learning problems. If you are interested in working with me, please send me an email with your CV, transcript, a description of your research interests, and other related materials.

Research Interests

My research interest includes topics in Machine Learning and Artificial Intelligence. More specifically, I am interested in

• Causal inference, especially on some topics regarding latent variables;
• Decision-making methods, including causal-effect-based method and Rehearsal learning;
• Causality and Large Language Models .

Publication

• Estimating possible causal effects with latent variables via adjustment and novel rule orientation.[PDF]

  Tian-Zuo Wang, Lue Tao, Tian Qin, and Zhi-Hua Zhou.

  Artificial Intelligence, 2025.

• Polynomial-delay MAG listing with novel locally complete orientation rules. (Oral, 0.99%, out of 12,107 papers)[PDF][Code]

  Tian-Zuo Wang, Wen-Bo Du, and Zhi-Hua Zhou.

  In: Proceedings of the 42nd International Conference on Machine Learning (ICML'25), Vancouver, Canada, 2025.

• Enabling optimal decisions in rehearsal learning under CARE condition.

  Wen-Bo Du, Hao-Yi Lei, Lue Tao, Tian-Zuo Wang, and Zhi-Hua Zhou.

  In: Proceedings of the 42nd International Conference on Machine Learning (ICML'25), Vancouver, Canada, 2025.

• Strong and weak identifiability of optimization-based causal discovery.

  Mingjia Li, Hong Qian, Tian-Zuo Wang, ShujunLi, Min Zhang, and Aimin Zhou.

  In: Proceedings of the 42nd International Conference on Machine Learning (ICML'25), Vancouver, Canada, 2025.

• Avoiding undesired future with sequential decisions.

  Lue Tao, Tian-Zuo Wang, Yuan Jiang, and Zhi-Hua Zhou.

  In: Proceedings of the 34th International Joint Conference on Artificial Intelligence (IJCAI'25), Montreal, Canada, 2025.

• Gradient-based nonlinear rehearsal learning with multivariate alterations. (Oral, 1.54%, out of 12,957 papers)[PDF]

  Tian Qin, Tian-Zuo Wang, and Zhi-Hua Zhou.

  In: Proceedings of the 39th AAAI Conference on Artificial Intelligence (AAAI'25), Philadelphia, PA, 2025.

• Avoiding undesired futures with minimal cost in non-stationary environments.[PDF]

  Wen-Bu Du, Tian Qin, Tian-Zuo Wang, and Zhi-Hua Zhou.

  In: Advances in Neural Information Processing Systems 37 (NeurIPS'24), Vancouver, Canada, 2024.

• Gradient-based causal discovery with latent variables.[PDF]

  Haotian Ni, Tian-Zuo Wang, Hong Tao, Xiuqi Huang, and Chenping Hou.

  Machine Learning, 2024.

• An efficient maximal ancestral graph listing algorithm. (Spotlight, 1.98%, out of 9,473 papers)[PDF][Code]

  Tian-Zuo Wang, Wen-Bo Du, and Zhi-Hua Zhou.

  In: Proceedings of the 41st International Conference on Machine Learning (ICML'24), Vienna, Austria, 2024.

• Rehearsal learning for avoiding undesired future.[PDF]

  Tian Qin, Tian-Zuo Wang, and Zhi-Hua Zhou.

  In: Advances in Neural Information Processing Systems 36 (NeurIPS'23), New Orleans, Louisiana, 2023.

• Tracking treatment effect heterogeneity in evolving environments.[PDF]

  Tian Qin, Long-Fei Li, Tian-Zuo Wang, and Zhi-Hua Zhou.

  Machine Learning, 2023

• 基于专家知识的主动因果效应辨识.[PDF]

  王天佐, 周志华.

  中国科学：信息科学, 2023.

• Sound and complete causal identification with latent variables given local background knowledge.[PDF]

  Tian-Zuo Wang, Tian Qin, and Zhi-Hua Zhou.

  Artificial Intelligence, 2023.

• Estimating possible causal effects with latent variables via adjustment.[PDF][Code]

  Tian-Zuo Wang, Tian Qin, and Zhi-Hua Zhou.

  In: Proceedings of the 40th International Conference on Machine Learning (ICML'23), Hawaii, Honolulu, 2023.

• Learning causal structure on mixed data with tree-structured functional models.[PDF]

  Tian Qin, Tian-Zuo Wang, and Zhi-Hua Zhou.

  In: Proceedings of the 23rd SIAM International Conference on Data Mining (SDM'23), Minneapolis, Minnesota, 2023.

• Sound and complete causal identification with latent variables given local background knowledge.[PDF][Code]

  Tian-Zuo Wang, Tian Qin, and Zhi-Hua Zhou.

  In: Advances in Neural Information Processing Systems 35 (NeurIPS'22), New Orleans, Louisiana, 2022.

• Actively identifying causal effects with latent variables given only response variable observable.[PDF]

  Tian-Zuo Wang and Zhi-Hua Zhou.

  In: Advances in Neural Information Processing Systems 34 (NeurIPS'21), Online, 2021.

• Budgeted heterogeneous treatment effect estimation.

  Tian Qin, Tian-Zuo Wang, and Zhi-Hua Zhou.

  In: Proceedings of the 38th International Conference on Machine Learning (ICML'21), Online, 2021.

• Cost-effectively identifying causal effects when only response variable is observable.[PDF][Supp][Code][Poster]

  Tian-Zuo Wang, Xi-Zhu Wu, Sheng-Jun Huang, and Zhi-Hua Zhou.

  In: Proceedings of the 37th International Conference on Machine Learning (ICML'20), Online, 2020.

• Towards identifying causal relation between instances and labels.[PDF][Code]

  Tian-Zuo Wang, Sheng-Jun Huang, and Zhi-Hua Zhou.

  In: Proceedings of the 19th SIAM International Conference on Data Mining (SDM'19), Alberta, Canada, 2019.

Talks

• Estimating Causal Effects within Markov Equivalence Class in the Presence of Latent Confounders. [Slides]

  2025.07, 第七届泛太平洋因果推断大会 The 7th Pacific Causal Inference Conference (PCIC 2025)

• 隐变量影响下的因果推断研究

  2024.11.02, 第二十二届机器学习及其应用研讨会 (MLA 2024)

• Recent advances in causal inference via partial ancestral graphs: IDA under latent confounders and MAG enumeration

  2024.11.02, 2024数据科学前沿国际研讨会 (ICFDS 2024)

Academic Service

Reviewer for Conferences: ICML(2025,2024,2023,2022,2021), NeurIPS(2025,2024,2023,2022,2021,2020), ICLR(2023,2022,2021), UAI(2023,2022), IJCAI(2025,2022,2021), ECAI(2020), AAAI(2023,2019), CCML(2019), PRICAI(2018)

Reviewer for Journals: Artificial Intelligence, Frontiers of Computer Science, 中国科学：信息科学, SCIENCE CHINA Information Sciences, Fundamental Research, TMLR, TKDD

Reviewer for Workshop: WHY 21@NeurIPS(2021)

IJCLR-2025 Organizing Committee Member (Link)

MLA-2023 Organizing Committee Member (Link)

MLA-2018 Workflow chair (Link)

Awards & Honors

1. 2024年度博士后创新人才支持计划

2. 2024年小米青年学者-科技创新奖

3. Nanjing University Yuxiu Scholar Program (2023年南京大学“毓秀青年学者”计划)

4. Outstanding Reviewer for ICML 2022 (top 10%)

5. Program A for Outstanding PhD candidate of Nanjing University (16 people/year)

6. National Scholarship for Doctoral Students, MOE of PRC, 2021

Correspondence

Email: wangtz@lamda.nju.edu.cn, wangtz1994@gmail.com

Office: Room 910, Computer Science Building, Xianlin Campus of Nanjing University

Address: Tian-Zuo Wang, National Key Laboratory for Novel Software Technology, Nanjing University, Xianlin Campus Mailbox 603, 163 Xianlin Avenue, Qixia District, Nanjing 210023, China
(南京市栖霞区仙林大道163号, 南京大学仙林校区603信箱, 软件新技术国家重点实验室, 210023.)