Zhiheng Zhang（张智恒）

Zhiheng Zhang（张智恒）

I am an interdisciplinary researcher primarily focusing on causal inference.

Zhiheng Zhang（张智恒）@ CAUSAL lab @ SUFE

Welcome to Zhiheng’s Lab! This lab is called CAUSAL (Causal Analysis for Underlying Structures And Learning), focusing on developing principled methods for causal analysis by uncovering underlying structures and integrating them with modern learning and decision-making systems.

欢迎访问张智恒的主页！本实验室名为CAUSAL (Causal Analysis for Underlying Structures And Learning)，致力于发展有理论基础和落地应用的因果推断方法，聚焦于潜在结构的刻画，并将其与现代学习算法和决策系统融合。

Zhiheng Zhang (pronunciation: Zhee-hung Jahng) is a tenure-track Assistant Professor in the School of Statistics and Data Science at the Shanghai University of Finance and Economics, starting from 2025.08. Previously, he received his Ph.D. from the Institute for Interdisciplinary Information Sciences, Tsinghua University. Zhiheng Zhang is the co-Principal Investigator of the 2025 CCF-DiDi Gaia Collaborative Research Fund project titled “Sequential Decision-Making for Long-Term Causal Effects Based on Reinforcement Learning” and the area chair of AAAI2025 AICT track. He is looking for PhD/Master/Interns.

张智恒担任上海财经大学统计与数据科学学院的常任轨助理教授，将于2025年8月正式入职。此前，他于清华大学交叉信息研究院（IIIS）获得博士学位。张智恒担任2025年度CCF-滴滴盖亚联合科研基金项目“基于强化学习的长期因果效应序贯决策问题”的联合负责人, AAAI 2025 人工智能会议 Causal Techniques 方向（AICT track）领域主席等。他正在寻找博士生/硕士生/实习生。

📢 招生信息 / Recruitment

我正在寻找若干 博士(2026入学)/硕士生（2025、2026入学）。同时也长期招收科研实习生（支持远程线上实习）。

我对博士生的基本期望是：

  • 具备良好的品德和强烈的内驱力，喜欢科研探索（鼓励自主探索感兴趣的研究方向——我会在能力范围内全力指导；在能力范围外帮助建立合作）；
  • 满足以下两项中的任意一项：
    • 扎实的数学基础（尤其是概率统计，专业不限）；
    • 出色的编程能力。
我对硕士生的基本期望是：
  • 对于有志于学界的，我会跟博士生共同带领你进行科研；并支持你的继续深造；
  • 对于有志于业界的，我能够将你推荐到相关方向的前沿研究部门（例如滴滴）实习。
如果你对上述研究方向感兴趣，欢迎随时与我联系，邮箱studyzzh@163.com。

Research Statement

He is an interdisciplinary researcher centered on causal inference, with research interests spanning experimental design, online learning, social networks, and partial identification. His work focuses on understanding and constructing the fundamental components of causal learning systems, aiming to address the following key questions:

  1. In observational studies, how can we uncover the transmission mechanisms among model assumptions, observed data, and identifiable boundaries?

  2. In experimental design, how can we characterize the performance limits arising from the interaction between scenario-specific features, algorithmic design, and estimator structure?

  3. In online learning and decision-making, how can we elucidate the mathematical foundations underlying the diverse optimization objectives across machine learning, economics, and statistical inference, such as regret and statistical power?

His long-term goal is to promote a unified integration of causal inference and machine learning systems, approached from two complementary directions:

  • On the one hand, to advance causal system design from a machine learning perspective, including:
    (i) relaxed input assumptions; (ii) more efficient estimation structures; (iii) output forms with broader generalization;

  • On the other hand, to explore how causal thinking can contribute back to foundational disciplines such as:
    social network analysis, game-theoretic modeling, and optimization algorithm design,
    further supporting decision-making in practical applications like recommendation systems, dispatching mechanisms, and market intervention strategies.

His representative research works aim to establish a closed-loop paradigm of “identification–estimation–policy optimization”, seeking a unified modeling pathway for “assumptions–structures–inference objectives”, and promoting the integration of causal learning theory and real-world applications.

Open to discussions and collaborations at any time. You can send him an email or add his WeChat.

他是一名以因果推断为核心的交叉学科研究者，研究兴趣涵盖 实验设计、在线学习、社交网络 以及 部分识别 等议题。他的研究聚焦于理解和建构因果学习系统中的基本组成要素，并试图回答以下关键问题：

  1. 在观测性研究中，如何揭示“模型假设—观测数据—可识别边界”之间的传导规律？
  2. 在实验设计中，如何刻画“应用场景特性—算法设计与估计器结构—统计效率”所呈现的性能极限？
  3. 在在线学习与决策中，如何阐明机器学习、经济管理与统计推断等领域多元优化目标之间的数学本质关联？

他的长期目标是推动 因果推理与机器学习系统的融合统一，从两个互补方向展开：

  • 一方面，从机器学习角度推动因果系统的设计，包括：
    (i) 更宽松的输入假设；(ii) 更高效的估计结构；(iii) 更具泛化性的输出形式；
  • 另一方面，探索如何将因果视角反哺其他基础领域的理论与方法，例如：
    社交网络分析、博弈论建模、优化算法设计 等，
    并进一步支撑 推荐系统、派单机制、市场干预策略 等现实应用场景中的决策优化。

他的代表性研究成果尝试构建了“识别-估计-策略优化”的闭环范式，寻求“假设-结构-推断目标”的统一建模路径，致力于推动因果学习理论与应用的结合。

他欢迎相关的交流或合作。若感兴趣，可邮件联系或添加他的WeChat。

Selected Honours

2018 Chinese Undergraduate Mathematics Competition Final (CMC), Gold Medal (Top 10 in China)

Academic Service

Reviewer: ICML, NeurIPs, ICLR, AISTATS, UAI, ACM Transactions on Information Systems (TOIS), Transactions of Mobile Computing (TMC), Journal of Machine Research (JMLR), Journal of the Royal Statistical Society, Series B (JRSSB)

Area Chair: AAAI 2025, Artificial Intelligence with Causal Techniques (AICT) track

Teaching

  1. Probability and Statistics (Tsinghua, 30470303-0), Yao Class TA, 2020-2021, Fall
  2. Probability and Statistics (Tsinghua, 30470303-0), Yao Class TA, 2021-2022, Fall
  3. Frontiers of Causal Inference (Tsinghua, 80470282-0): Graduate TA for Tsinghua University, 2022-2023, Spring
  4. Probability and Statistics (Tsinghua, 30470303-0), Yao Class TA, 2023-2024, Fall
  5. Frontiers of Causal Inference (Tsinghua, 80470282-0): Graduate TA for Tsinghua University, 2023-2024, Spring
  6. Advanced Applied Probability (Tsinghua, 40470503-0), Yao Class TA, 2024-2025, Fall
  7. Frontiers of Causal Inference (Tsinghua, 80470282-0): Graduate TA for Tsinghua University, 2024-2025, Spring
  8. 概率论 （上海财经大学，105494），统计与数据科学学院，本科生课，授课教师，2025-2026, Fall
  9. 数据分析与统计建模 （上海财经大学，213488），统计与数据科学学院，研究生课，授课教师，2025-2026，Fall

Invited Talks

TitleVenueDate
Conceptual Mathematics, Article VITsinghua FODS Seminar2022.11
Tight Partial Identification of Causal EffectsAI Time2024.06
 ICML 2024, Vienna2024.06
 Tsinghua Yao Class Graduate Forum2024.12
 Shanghai Jiao Tong University John Hopcroft Center2025.01
 Bear Conference (Goxiong Hui)2025.06
A Systematic ML Framework for CausalityPeking University Causality Seminar2024.11
 13th National Conference on Probability Statistics2024.11
 National University of Defense Technology2025.02
 Xi’an Jiaotong University2025.03
 Shanghai University of Finance and Economics2025.03
 Shandong University2025.03
 National University of Defense Technology2025.03
Partial Identification with Proxy of Latent ConfoundingsUAI 2024, Barcelona2024.06
Robust Causal Inference for Recommender SystemSIGIR 2023, Taiwan2023.07
Adjusting Auxiliary Variables Under Approximate Neighborhood InterferenceTsinghua University2025.04
 Bear Conference (Goxiong Hui)2025.06
Online Experiment Design under Interference Estimation-regret TradeoffShanghai Jiao Tong University John Hopcroft Center2025.01
 10th Statistical Forum2025.04