I am currently a PhD student in Stanford ICME, advised by Vasilis Syrgkanis. Prior to joining Stanford, I received by Bachelors degree in Mathematics and Bioengineering from UCLA. I am broadly insterested in the intersection of machine learning and causal inference.

Publications

Lan, Hui, and Vasilis Syrgkanis. “Causal Q-Aggregation for CATE Model Selection.” https://doi.org/10.48550/arXiv.2310.16945.

Abstract: Accurate estimation of conditional average treatment effects (CATE) is at the core of personalized decision making. While there is a plethora of models for CATE estimation, model selection is a nontrivial task, due to the fundamental problem of causal inference. Recent empirical work provides evidence in favor of proxy loss metrics with double robust properties and in favor of model ensembling. However, theoretical understanding is lacking. Direct application of prior theoretical work leads to suboptimal oracle model selection rates due to the non-convexity of the model selection problem. We provide regret rates for the major existing CATE ensembling approaches and propose a new CATE model ensembling approach based on Q-aggregation using the doubly robust loss. Our main result shows that causal Q-aggregation achieves statistically optimal oracle model selection regret rates of log(M)n (with M models and n samples), with the addition of higher-order estimation error terms related to products of errors in the nuisance functions. Crucially, our regret rate does not require that any of the candidate CATE models be close to the truth. We validate our new method on many semi-synthetic datasets and also provide extensions of our work to CATE model selection with instrumental variables and unobserved confounding.

Teaching

• Course Assistant for Applied Causal Inference with Machine Learning and AI (MS&E 228, Stanford)