Online interdisciplinary seminar series on statistical methodology for social and behavior research(January 29)

This session is jointly sponsored by the Statistics department and the Research Methods, Measurement, and Evaluation program, University of Connecticut (UCONN), New England Statistical Society (NESS) and Statistical and Applied Mathematical Institute (SAMSI) as part of online interdisciplinary seminar series on statistical methodology for social and behavior research.

Speaker: P. Richard Hahn

Date and Time: JANUARY 29, 2021- 12:00-1:15PM EST



In this talk, I will describe recent work on Bayesian supervised learning for conditional average treatment effects. I will motivate the proposed Bayesian causal forest model in terms of fixing two specific flaws with previous approaches. One, our model allows for direct regularization of the treatment effect function, providing lower variance estimates of heterogeneous treatment effects. Two, by including an estimate of the propensity score as a control variable in our model we mitigate a phenomenon called “regularization induced confounding” that leads to substantial bias in previous approaches. I will conclude with a detailed discussion of designing simulation studies to systematically investigate and validate machine learning models for causal inference.

Note: Dr. Hahn may also talk about this tutorial a bit:

Bio: Professor P. Richard Hahn has a B.A. in Philosophy of Science from Columbia University and earned his PhD in Statistics from Duke University in 2011. He taught at University of Chicago Booth School of Business for seven years before joining the School of Mathematical and Statistical Sciences at Arizona State University in 2017. His research lies at the intersection of machine learning and causal inference, specifically tree based regression methods for estimating heterogeneous treatment effects. Other research interests include latent variable models and statistical decision theory. He enjoys road trips in the mountain southwest with his family and riding and working on bicycles.

Join by video system

You can also dial and enter your meeting number.
Join by phone
+1-415-655-0002 US Toll
Access code: 120 084 6708

Detailed Connection Information:

For inquiry, please contact Dr. Xiaojing Wang at