I'm currently a Ph.D. student in Economics at MIT, where I'm also part of the Interdisciplinary Ph.D. in Statistics (IDPS) program. I received my bachelor's degree in Economics and Statistics from the University of Chicago in 2017 and completed the Stanford GSB Research Fellows Program in 2019. My main research fields are econometrics and public finance. I work on adapting machine learning and high-dimensional statistical tools for causal inference, particularly in panel data contexts. I aim to create methods helpful for applied analysis in microeconomics, particularly for agricultural and climate economics.

Research

Estimating Continuous Treatment Effects in Panel Data using Machine Learning with a Climate Application (2022)
Sylvia Klosin and Max Vilgalys.
[arXiv] [github]
abstract
This paper introduces and proves asymptotic normality for a new semi-parametric estimator of continuous treatment effects in panel data. Specifically, we estimate the average derivative. Our estimator uses the panel structure of data to account for unob- servable time-invariant heterogeneity and machine learning (ML) methods to preserve statistical power while modeling high-dimensional relationships. We construct our estimator using tools from double de-biased machine learning (DML) literature. Monte Carlo simulations in a nonlinear panel setting show that our method estimates the average derivative with low bias and variance relative to other approaches. Lastly, we use our estimator to measure the impact of extreme heat on United States (U.S.) corn production, after flexibly controlling for precipitation and other weather features. Our approach yields extreme heat effect estimates that are 50% larger than estimates using linear regression. This difference in estimates corresponds to an additional $3.17 billion in annual damages by 2050 under median climate scenarios. We also estimate a dose-response curve, which shows that damages from extreme heat decline somewhat in counties with more extreme heat exposure.

Bagged Polynomial Regression and Neural Networks (2022)
Sylvia Klosin and Jaume Vives-i-Bastida.
[arXiv]

Automatic Double Machine Learning for Continuous Treatment Effects (2021).
Sylvia Klosin. [arXiv]

Software

hdm High-Dimensional Metrics (R package).
Based on Klosin and Vilgalys (2022) and others.
[github] [example code]

Teaching

Statistical Method in Economics (Graduate) - MIT 14.380.
Teaching Assistant, with Professor Anna Mikusheva. Fall 2021.

Applied Econometrics (Graduate) - MIT 14.381.
Teaching Assistant, with Professor Whitney Newey. Fall 2021.