Statistics and Data Science Seminar - David Bruns-Smith
— 4:30pm
Location:
In Person
-
Steinberg Auditorium, Baker Hall A53
Speaker:
DAVID BRUNS-SMITH
,
Postdoctoral Fellow, Stanford Data Science, Graduate School of Business, Stanford University
https://brunssmith.com/
The growing access to large administrative datasets with rich covariates presents an opportunity to revisit classic two-stage least squares (2SLS) applications with machine learning (ML). We develop Two-Stage Machine Learning, a simple and efficient estimator for nonparametric instrumental variables (NPIV) regression. Our method uses ML models to flexibly estimate nonparametric treatment effects while avoiding the computational complexity and statistical instability of existing machine learning NPIV approaches. Our procedure has two steps: first, we predict the outcomes given instruments and covariates (the reduced form) and extract a basis from this predictor; second, we predict the outcomes using the treatment and covariates, but where the predictions are projected onto the learned basis of instruments. We prove that under a testable condition, our estimation error depends entirely on the reduced-form prediction task, where ML methods excel. We also develop a bias correction procedure that provides valid confidence intervals for scalar summaries like average derivatives. In an empirical application to California supermarket data featuring bunching at 99-ending price points, we find our machine learning approach is crucial for modeling discontinuities in demand at the dollar boundary: we reduce NPIV estimation error nearly eight-fold compared to previous estimators and estimate a price elasticity that is 2.5-6 times larger than prior estimates.
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David Bruns-Smith is a Postdoctoral Fellow at Stanford Data Science working with Guido Imbens. His research focuses on machine learning methods for causal inference with an emphasis on applications in macroeconomics. David's recent methodological research studies debiased machine learning, including for instrumental variables regression and reinforcement learning. He then applies these causal machine learning methods to a variety of economics questions including fiscal stimulus, monetary policy, and the evolution of income inequality.
Previously, David completed his PhD in Computer Science at UC Berkeley, advised by Avi Feller and Emi Nakamura. In addition to Computer Science, he completed core PhD courses in Economics as a Berkeley Opportunity Lab Labor Science Fellow. From 2023-2024, he worked with Alex D'Amour at Google Deepmind.
For More Information:
jpaschke@andrew.cmu.edu