I am an Assistant Professor in Emory's Quantitative Theory & Methods Department. I work on methods for estimating causal effects from observational studies and experiments and sometimes on the R packages synthdid and grf.

This spring, I'm teaching Machine Learning Theory (QTM 490R-1).

Before coming to Emory, I did a PhD at Columbia with Arian Maleki and José Zubizarreta and a postdoc at Stanford with Susan Athey, Guido Imbens, and Stefan Wager. Years ago, I was a computer vision researcher in Michael Black's group at Brown and MPI Tübingen.

Preprints and Publications

Statistics

• Large-Sample Properties of the Synthetic Control Method under Selection on Unobservables
  Dmitry Arkhangelsky and David A. Hirshberg
  Preprint
  [arXiv]
• A Stable and Efficient Covariate-Balancing Estimator for Causal Survival Effects
  Khiem Pham, David A. Hirshberg, Phuong-Mai Huynh-Pham, Michele Santacatterina, Ser-Nam Lim, and Ramin Zabih
  Preprint
  [arXiv]
• Perception of Increasing Wildfire Risk Lowers Appreciation of Residential Real Estate in California
  Xinkun Nie, Kiran Chawla, Daniel Irvin, David A. Hirshberg, Stefan Wager, Michael W. Wara, and Ines Azevedo
  Preprint
  [SSRN]
• The Balancing Act in Causal Inference
  Eli Ben-Michael, Avi Feller, David A. Hirshberg, and José R. Zubizarreta
  Preprint
  [arXiv]
• Least Squares with Error in Variables
  David A. Hirshberg
  Preprint
  [arXiv]
• Minimax Linear Estimation of the Retargeted Mean
  David A. Hirshberg, Arian Maleki, and José R. Zubizarreta
  Preprint
  [arXiv]
• Synthetic Difference in Differences
  Dmitry Arkhangelsky, Susan Athey, David A. Hirshberg, Guido W. Imbens, and Stefan Wager
  American Economic Review, 2021
  [arXiv] [slides] [github]
• Augmented Minimax Linear Estimation
  David A. Hirshberg and Stefan Wager
  Annals of Statistics, 2021
  [arXiv] [slides] [youtube] [github]
• Off-Policy Evaluation via Adaptive Weighting with Data from Contextual Bandits
  Ruohan Zhan, Vitor Hadad, David A. Hirshberg, and Susan Athey
  ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021
  [arXiv]
• Confidence Intervals for Policy Evaluation in Adaptive Experiments
  Vitor Hadad, David A. Hirshberg, Ruohan Zhan, Stefan Wager, and Susan Athey
  Proceedings of the National Academy of Sciences, 2021
  [arXiv] [publisher site]
• Debiased Inference of Average Partial Effects in Single-Index Models: Comment on Wooldridge and Zhu
  David A. Hirshberg and Stefan Wager
  Journal of Business and Economic Statistics, 2020
  [arXiv] [publisher site]
• On Two Approaches to Weighting in Causal Inference: Comment on Setodji et al.
  David A. Hirshberg and José R. Zubizarreta
  Epidemiology, 2017
  [preprint] [publisher site]

Computer Vision and Graphics

• Coregistration: Simultaneous alignment and modeling of articulated 3D shape
  David A. Hirshberg, Matthew Loper, Eric Rachlin, and Michael J. Black
  European Conference on Computer Vision (ECCV), 2012
  [paper] [poster] [project page] [supplemental material] [publisher site]
• DRAPE: DRessing Any PErson
  Peng Guan, Loretta Reiss, David A. Hirshberg, Alexander Weiss, and Michael J. Black
  ACM Transactions on Graphics (SIGGRAPH Proceedings), 2012
  [paper] [slides] [project page] [video]
• Home 3D body scans from noisy image and range data
  Alexander Weiss, David A. Hirshberg, and Michael J. Black
  International Conference on Computer Vision (ICCV), 2011
  [paper] [poster] [project page] [video]
• Evaluating the Automated Alignment of 3D Human Body Scans
  David A. Hirshberg, Matthew Loper, Eric Rachlin, Aggeliki Tsoli, Alexander Weiss, Brian Corner, and Michael J. Black
  International Conference on 3D Body Scanning Technologies, 2011
  [paper] [slides] [project page]

Recent Classes

Machine Learning Theory	Spring 2025
Prediction, Inference, and Causality	Fall 2024