David Alexander Bruns-Smith

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David Alexander Bruns-Smith

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David Bruns-Smith joins MIT as an Assistant Professor with a shared appointment between the MIT Sloan Finance Group and the EECS (Electrical Engineering and Computer Science) Department.

His research develops machine-learning methods for causal inference with applications in macroeconomics and household finance.

Previously, he was a postdoctoral fellow at Stanford Data Science and received a PhD in computer science from UC Berkeley in 2024.

Publications

"Augmented Balancing Weights as Linear Regression."

Bruns-Smith, David, Oliver Dukes, Avi Feller, and Elizabeth L Ogburn. Journal of the Royal Statistical Society Series B: Statistical Methodology. Forthcoming.

"Deconfounding Scores and Representation Learning for Causal Effect Estimation with Weak Overlap."

Oscar Clivio, Alexander D'Amour, Alexander Franks, David Bruns-Smith, Chris Holmes, and Avi Feller. In Proceedings of The 29th International Conference on Artificial Intelligence and Statistics, Tangier, Morocco:. Forthcoming. arXiv.

"Ridge Boosting is Both Robust and Efficient."

David Bruns-Smith, Zhongming Xie, and Avi Feller. In Advances in Neural Information Processing Systems, San Diego, CA: September 2025. arXiv.

"Using Supervised Learning to Estimate Inequality in the Size and Persistence of Income Shocks."

David Bruns-Smith, Avi Feller, and Emi Nakamura. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, Chicago, IL: June 2023.

"Outcome Assumptions and Duality Theory for Balancing Weights."

David Bruns-Smith and Avi Feller. In Proceedings of The 25th International Conference on Artificial Intelligence and Statistics, Virtual Conference: March 2022. arXiv.

"Model-Free and Model-Based Policy Evaluation when Causality is Uncertain."

David Bruns-Smith. In Proceedings of the 38th International Conference on Machine Learning, Virtual Conference: July 2021. arXiv.

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