Reevaluating Causal Estimation Methods with Data from a Product Release (with Justin Young)
Machine learning methods estimate flexible relationships between confounders, treatments and outcomes, making unconfoundedness assumptions in causal analysis more palatable. How successful are these approaches in recovering ground truth baselines? We analyze new data covering an experimental rollout of a software product feature for one group of users and a voluntary opt-in to the same feature for another similar group. Recovering causal effects from nonrandomized data is feasible--but only with careful modeling choices. We offer best practices for more credible treatment effect estimation in modern, high-dimensional datasets and evaluate which tests provide meaningful signals about the reliability of estimated effects.
Do Workforce Development Programs Bridge the Skills Gap? (with Lisa B. Kahn, Joanna Venator, and Michael Dalton)
Under review
Most U.S. states have workforce development programs that offer firms grants to train their own workers. We create unique data linkages between participating firms, employment, and vacancies to explore the determinants and consequences of such programs. Training grants are more prevalent in markets where firms face greater employee poaching risk, suggesting these programs help overcome a market failure in updating worker skills. After training, firms experience prolonged employment growth and downskilling in their job posts, relative to a matched control group. Training appears to help firms move toward optimal scale and expand opportunities for less skilled workers.
Shifting Work Patterns with Generative AI (with Sonia Jaffe, Nicole Immorlica, and Christopher Stanton), American Economic Review: Insights (forthcoming)
Coverage: Marginal Revolution
A Meta-learner for Heterogeneous Effects in Difference-in-Differences (with Hui Lan, Haoge Chang, Vasilis Syrgkanis), , Proceedings of the 42nd International Conference on Machine Learning (ICML), 2025.
A Causal AI Suite for Decision-Making (with Emre Kiciman, Darren Edge, Adam Foster, Agrin Hilmkil, Joel Jennings, Chao Ma, Robert Ness, Nick Pawlowski, Amit Sharma, Cheng Zhang), NeurIPS 2022 Workshop on Causality for Real-world Impact
Estimating the Long-term Effects of Novel Treatments (with Keith Battocchi, Maggie Hei, Greg Lewis, Miruna Oprescu and Vasilis Syrgkanis), Proceedings of the 34th Conference on Neural Information Processing Systems (NeurlPS), 2021
The Consequences of Academic Match between Students and Colleges (with Jeffrey Smith), Journal of Human Resources, 55, no. 3 (July 2020), 767-808
Coverage: Forbes
Risk and Return Tradeoffs in Lifetime Earnings, Journal of Labor Economics, 36, no. 4 (October 2018), 981-1021
The College Earnings Premium and Changes in College Enrollment: Testing Models of Expectation Formation, Labour Economics, 49 (December 2017), 84-94
Determinants of the Match between Student Ability and College Quality (with Jeffrey Smith), Journal of Labor Economics, 35, no. 1, (January 2017), 45-66
Preprint, Online Appendix, NBER version
Coverage: Inside Higher Ed, Chronicle
AI-Assisted Programming and Labor Demand (with Lindsey Raymond) Slides
The Impact of AI on Career Ladders (with Dana Scott)
Who Should Build Workplace AI Agents? (with Alina Andreeva, Iavor Bojinov, Daniel Goldstein, Paul Hamilton, Annika Hildebrandt, Michael Menietti, and Shaolong Wu)
Peer Influence Can Make or Break Your AI Rollout (with Nancy Baym and Sonia Jaffe), Harvard Business Review, March 2026. SSRN paper
The Agentic Economy (with David Rothschild, Markus Mobius, Jake Hofman, Daniel Goldstein, Nicole Immorlica, Sonia Jaffe, Brendan Lucier, Aleksandrs Slivkins, and Matthew Vogel) Communications of the ACM, 2026, 69(2), pp. 39-42. arXiv version
From Augmentation to Reconstruction: Guiding the AI Disruption to the Good Place (with David M. Rothschild, Jake M. Hofman, Markus Mobius, Brendan Lucier, Eleanor Dillon, Daniel G. Goldstein, Nicole Immorlica, Aleksandrs Slivkins), , Microsoft whitepaper, 2026
Early Impacts of M365 Copilot (with Sonia Jaffe, Sida Peng, Alexia Cambon), Microsoft whitepaper, 2025
Comment on Education and Innovation in The Role of Innovation and Entrepreneurship in Economic Growth, 2022.
Be careful when interpreting predictive models in search of causal insights (with Jacob LaRiviere, Scott Lundberg, Jonathan Roth, Vasilis Syrgkanis) Medium, 2021.
Why a Dollar Depreciation May Not Close the U.S. Trade Deficit (with Linda Goldberg), Federal Reserve Bank of New York Current Issues in Economics and Finance, 2007.
Self-Employment Dynamics and the Returns to Entrepreneurship (with Chris Stanton)
Coverage: USA Today
The Role of Sorting and Skill Prices in the Evolution of the College Premium (with Greg Veramendi)