The December 2nd, 2016 Workshop on Quantitative Methods in Education, Health, and the Social Sciences (QMeHSS) will take place from 10:30am - 12:00pm in NORC Conference room 344. Richard Hahn from the University of Chicago will be leading this workshop. We hope to see you there. NORC is located at 1155 E. 60th Street.
Bayesian Causal Forests
P. Richard Hahn
Associate Professor of Econometrics and Statistics, Chicago Booth School of Business , University of Chicago
Abstract: In this talk I will describe a semi-parametric Bayesian regression model for estimating heterogeneous treatment effects from observational data. Standard nonlinear regression models, which may work quite well for prediction, can yield badly biased estimates of treatment effects when fitted to data with strong confounding. The new Bayesian causal forest model is able to eliminate this adverse bias by jointly modeling the treatment and the response conditional on control variables. Two empirical illustrations are given, analyzing the impact of smoking on medical expenditures and the impact of abortion laws on future crime rates.
Bio: P. Richard Hahn is Associate Professor of Econometrics and Statistics. His research develops computational methods for modeling complex real-world data. Currently he is developing statistical tools for analyzing data from personal health technology to inform training and recovery programs for professional athletes. His research has appeared in the Journal of the American Statistical Association, the Annals of Applied Statistics, the Journal of Business and Economic Statistics, and the Journal of the Royal Statistical Society. Outside of academia, Hahn has statistical consulting experience in diverse areas, including politics, management, marketing and biotech. Hahn earned his PhD in statistical science from Duke University