About Me

I am a Ph.D. student in Business Economics at Harvard Business School. I finished my A.B. and S.M. degrees in Applied Mathematics at Harvard University in 2019. My current research interests are econometrics and machine learning, applied to empirical work in industrial organization, market design, and labor economics.

I grew up in Shanghai, China. I have an Erdös number of 4.

Working Papers

“The (Non-) Effect of Opportunity Zones on Housing Prices” with Edward L. Glaeser and David Wessel

[NBER Working Paper] [Replication files]

Abstract Will the Opportunity Zone program, America’s largest new place-based policy in decades, generate neighborhood change? We compare single-family housing price growth in Opportunity Zones with price growth in areas that were eligible but not included in the program. We also compare Opportunity Zones to their nearest geographic neighbors. All estimates rule out price impacts greater than 1.3 percentage points with 95% confidence, suggesting that, so far, home buyers don’t believe that this subsidy will generate major neighborhood change. Opportunity Zone status reduces prices in areas with little employment, perhaps because buyers think that subsidizing new investment will increase housing supply.

“Causal Inference and Matching Markets”

Undergraduate thesis advised by Scott Duke Kominers and David C. Parkes. Awarded the Thomas T. Hoopes Prize at Harvard College.

[Simulable Mechanisms] [Cutoff Mechanisms] [Regression discontinuity with endogenous cutoff]

Abstract We consider causal inference in two-sided matching markets, particularly in a school choice context, where the researcher is interested in understanding the treatment effect of schools on students. We characterize two classes of mechanisms that can be considered natural experiments, simulable mechanisms and cutoff mechanisms, which are mathematically general and encompass a large set of allocation mechanisms used in practice. We propose estimation and inference procedures for causal effects given each of these mechanisms, and characterize the statistical properties of the resulting causal estimators. Our approach allows us to relax the simplifying large-market assumption made in earlier work (Abdulkadiroglu, Angrist, Narita, and Pathak 2017, 2019), and we show that classical regression discontinuity procedures extend to settings where the discontinuity cutoff is endogenously chosen. Our results provide a rigorous statistical basis for causal inference and program evaluation in a number of settings where treatment assignment is complex.

Auctions with Entry versus Entry in Auctions” with Scott Duke Kominers

Abstract We show that charging entry fees can sometimes dominate the benefit of recruiting additional bidders to auctions, even though the fees themselves implicitly reduce competition at the auction stage. We also highlight that admission fees and reserve prices are different instruments in a setting with uncertainty over entry costs, and that optimal mechanisms in such settings may be higher-dimensional than in Myerson (1981). Our results provide a counterpoint to the broad intuition of Bulow and Klemperer (1996) that market thickness often takes precedence over market power in auction design.

Under Preparation

“Inference on Functionals of Nonparametric Conditional Moments using Neural Networks” with Xiaohong Chen and Elie Tamer



  • As author:
    • Auxiliary functionality for pandas: pandas_tools. Provides functionalities that enhance DataFrame.to_latex for generating publication-quality tables with automatic formatting of floats (including scientific notation) and automatic streamlining of inputting a table in a master document.
    • binsreg in Python: binscatter. Wraps the binsreg library function in R (https://arxiv.org/pdf/1902.09608.pdf) and provides binned scatterplots in Python. binsreg offers theoretical results by seeing binned scatterplots as nonparametric regression, and offers plotting routines that are motivated by theoretical considerations.
  • As contributor: pyjanitor, namedtensor