I am a Ph.D. student in Econometrics and Statistics at the University of Chicago Booth School of Business, where I am advised by Azeem Shaikh and Christian Hansen. My primary research interests are in causal inference, including the design and analysis of experiments and observational studies. I am honored to have been awarded the Amazon Graduate Fellowship 2022, which enables me to work on industry-related problems in causal inference.
Publications and Forthcoming Papers
Inference for Matched Tuples and Fully Blocked Factorial Designs (with Yuehao Bai and Max Tabord-Meehan)
Quantitative Economics, 2024, 15(2), 279–330.
Revisiting the Analysis of Matched Pair and Stratified Experimental Designs in the Presence of Attrition (with Yuehao Bai, Meng Hsuan Hsieh, and Max Tabord-Meehan)
Journal of Applied Econometrics, 2024, 39(2), 256–268.
Proximal Causal Inference for Synthetic Control with Surrogates (with Eric J. Tchetgen Tchetgen and Carlos Varjão)
The 27th International Conference on Artificial Intelligence and Statistics (AISTATS). 2024
Learning Intuitive Policies Using Action Features (with Mingwei Ma, Samuel Sokota, Max Kleiman-Weiner, Jakob Foerster)
International Conference on Machine Learning (ICML), 2023
Working Papers
Inference for Two-stage Experiments under Covariate-Adaptive Randomization
Revision Requested at the Journal of Econometrics.
Inference in Cluster Randomized Trials with Matched Pairs (with Yuehao Bai, Azeem Shaikh and Max Tabord-Meehan)
Revision Requested at the Journal of Econometrics.
Efficient Treatment Effect Estimation in Observational Studies under Heterogeneous Partial Interference (with Zhaonan Qu, Ruoxuan Xiong and Guido Imbens)
We propose a flexible framework for heterogeneous partial interference that partitions units into subsets based on observables. We allow interactions to be heterogeneous across subsets, but homogeneous for individuals within a subset. In this framework, we propose a class of estimators for heterogeneous direct and spillover effects from observational data that are shown to be doubly robust, asymptotically normal, and semiparametric efficient.
On the Efficiency of Finely Stratified Experiments (with Yuehao Bai, Azeem Shaikh and Max Tabord-Meehan)
We study the efficient estimation of a large class of treatment effect parameters that arise in the analysis of experiments.