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.

CV

Publications and Forthcoming Papers

Inference for Matched Tuples and Fully Blocked Factorial Designs (with Yuehao Bai and Max Tabord-Meehan)

Forthcominig at the Quan­ti­ta­tive Eco­nom­ics.

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)

Forthcominig at the Jour­nal of Applied Econo­met­rics.

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 in Cluster Randomized Trials with Matched Pairs (with Yuehao Bai, Azeem Shaikh and Max Tabord-Meehan)

Revision Requested at the Jour­nal of Econo­met­rics.

Inference for Two-stage Experiments under Covariate-Adaptive Randomization

We study inference in two-stage randomized experiments with covariate-adaptive randomization. Here, by a two-stage randomized experiment, we mean one in which clusters (e.g., households, schools, or graph partitions) are first randomly assigned to different levels of treated fraction and then units within each treated clusters are randomly assigned to treatment or control according to its selected treated fraction; by covariate-adaptive randomization, we mean randomization schemes that first stratify according to baseline covariates and then assign treatment status so as to achieve “balance” within each stratum.

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.