About me
I am a Ph.D. candidate in Statistics at Stanford University, advised by Stefan Wager and Dominik Rothenhäusler. I am on the 2026-27 job market.
My research focuses on designing robust methods for causal inference in complex settings that arise in modern applications, with the same rigor and transparency as in classical designs.
Outside of research, I enjoy traveling, photography, movies, and chess. Before starting my Ph.D., I used to mentor high school students for Mathematical Olympiads and similar competitions; check out this old blog for resources.
Selected research
Dynamic Thresholding Designs
Threshold-based interventions are common in clinical practice, but studying their long-term impact is challenging due to temporal dynamics and carryover effects. We show that a dynamic marginal policy effect at the treatment threshold admits a simple, reduced-form characterization, and develop a local linear regression method for estimation and inference.
Specification-robust Causal Inference
Covariate adjustment is essential for removing confounding in observational studies, yet researchers often face uncertainty over which covariates to adjust for. Building on debiased machine learning, we establish valid inference for a reweighted target population without committing to a single specification—requiring only that at least one candidate adjustment is valid.
PLRD: Partially Linear Regression Discontinuity Inference
Regression discontinuity designs are widely used in applied economics, yet the existing methods often face a trade-off between nominal coverage and interval width. Building on the bias-aware literature, PLRD exploits a partially linear structure and higher-order smoothness to produce intervals that are both valid and short.
Other roles
I help organize the Online Causal Inference Seminar, a weekly seminar on causal inference with participants from all over the world.
Contact
389 Jane Stanford Way, Stanford University
Stanford, CA 94305