Posted on Monday, January 25, 2021
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
Topic: THE BAYESIAN CAUSAL FOREST MODEL: REGULARIZATION, CONFOUNDING, AND
HETEROGENEOUS EFFECTS
Abstract:
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: https://math.la.asu.edu/~prhahn/xbcf_demo.html
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.
For inquiry, please contact Dr. Xiaojing Wang at xiaojing.wang@uconn.edu
