About

I am a third-year PhD student in Management Science and Engineering at Stanford University, co-advised by Prof. Vasilis Syrgkanis and Prof. Jose Blanchet.

My research asks how far we can automate causal inference — from the statistical estimator to the AI agent — without giving up the mathematical guarantees, transparency, and domain sensitivity that scientific and policy applications require. It sits at the boundary of statistics, econometrics, machine learning, and human-centered AI.

Before Stanford, I studied mathematics at Fudan University. There I worked with Prof. Yinyu Ye on SOLNP+, a derivative-free solver for general nonlinear optimization, and with Prof. Zhaoran Wang and Prof. Zhuoran Yang on offline learning in zero-sum Markov games.

Research

• Partial identification

  When data and assumptions cannot pin down a single causal effect, they often still imply informative bounds. I build methods that compute those bounds with statistical guarantees.

• Automating Causal Inference

  Causal estimation is full of choices that today demand an expert: regularization, tuning, estimator selection. I make those choices data-driven while keeping the theory intact.

• Causal reasoning in AI

  Can we trust an AI system's causal reasoning? I build benchmarks that test it, methods that audit a model's internal computation, and Lean-based formal verification for causal results.

Read more about my research →

News

• May 2026New preprint: Bucketing the Good Apples: A Method for Diagnosing and Improving Causal Abstraction, with Puyin Li, Ahmad Jabbar, Thomas Icard, and Atticus Geiger.
• Apr 2026New preprint: Partial Identification of Policy-Relevant Treatment Effects with Instrumental Variables via Optimal Transport, with Vasilis Syrgkanis and Jose Blanchet.
• Mar 2026New preprint: Adaptive Estimation and Inference in Conditional Moment Models via the Discrepancy Principle, with Vasilis Syrgkanis.
• Feb 2026New preprint: CausalReasoningBenchmark: A Real-World Benchmark for Disentangled Evaluation of Causal Identification and Estimation, with Ayush Sawarni and Vasilis Syrgkanis.
• Oct 2025Talk on Consistency of Neural Causal Partial Identification at the INFORMS Annual Meeting.
• Sep 2025Estimation of Treatment Effects in Extreme and Unobserved Data was accepted at NeurIPS 2025.
• Jun 2025Started a summer internship as a Data Scientist at LinkedIn, working on long-term treatment effects with debiased machine learning.

Contact

The fastest way to reach me is jiyuantan@stanford.edu. I am always glad to chat about everything.