Richard Hahn

Education

• Doctor of Philosophy (Ph.D.) Duke University (2007 — 2011)
• Master of Science (M.S.) New Mexico Institute of Mining and Technology (2005 — 2007)
• Bachelor of Arts (B.A.) Columbia University in the City of New York (2002 — 2004)

Companies

• Associate Professor of Statistics Arizona State University (2017)
• Assistant Professor of Econometrics and Statistics University of Chicago Booth School of Business (2011 — 2017)
• Graduate Student Duke University (2007 — 2011)

Research interests:

• Bayesian machine learning for causal inference; Monte Carlo methods; decision theory; interpretable data science; semi-supervised learning.

Selected papers

• Andrew Herren and PRH. Semi-supervised learning and the case of true versus estimated propensity scores
• Demetrios Papakostas, PRH, Jared Murray, Frank Zhou, and Joseph Gerakos.Do forecasts of bankruptcy cause bankruptcy? A machine learning sensitivity analysis.
• Chelsea Krantsevich, PRH, Yi Zheng, and Charles Katz. Bayesian decision theory for tree-based adaptive screening tests with an application to youth delinquency.
• Nikolay Krantsevich, Jingyu He, and PRH. Stochastic tree ensembles for estimating heterogeneous treatment effects.
• PRH, Jared S. Murray, and Carlos M. Carvalho. Bayesian regression tree models for causal inference: regularization, confounding, and heterogeneous effects. Bayesian Analysis 15 (3), 965-1056.
• Yeager et al. A national experiment reveals where a growth mindset improves achievement. Nature volume 573,364–369 (2019).
• Jingyu He and PRH. Stochastic tree ensembles for regularized supervised learning. Journal of the American Statistical Association.

Teaching

• Bayesian Statistics
• Causal Inference
• Teaching Statement