Frauke Kreuter, Professor
Statistics and Data Science, Ludwig-Maximilians-University of Munich
Co-director, Data Science Centers at the University of Maryland (USA) and Mannheim (Germany)

Abstract:
TBA

Bio:
Frauke Kreuter is an elected fellow of the American Statistical Association and the 2020 recipient of the Warren Mitofsky Innovators Award of the American Association for Public Opinion Research. In addition to her academic work Dr. Kreuter is the Founder of the International Program for Survey and Data Science, developed in response to the increasing demand from researchers and practitioners for the appropriate methods and right tools to face a changing data environment; Co-Founder of the Coleridge Initiative, whose goal is to accelerate data-driven research and policy around human beings and their interactions for program management, policy development, and scholarly purposes by enabling efficient, effective, and secure access to sensitive data about society and the economy; and Co-Founder of the German language podcast Dig Deep.

Carlos M. Carvalho, Professor
The University of Texas McCombs School of Business

Abstract:
This paper presents a novel nonlinear regression model for estimating heterogeneous treatment effects, geared specifically towards situations with small effect sizes, heterogeneous effects, and strong confounding by observables. Standard nonlinear regression models, which may work quite well for prediction, have two notable weaknesses when used to estimate heterogeneous treatment effects. First, they can yield badly biased estimates of treatment effects when fit to data with strong confounding. The Bayesian causal forest model presented in this paper avoids this problem by directly incorporating an estimate of the propensity function in the specification of the response model, implicitly inducing a covariate-dependent prior on the regression function. Second, standard approaches to response surface modeling do not provide adequate control over the strength of regularization over effect heterogeneity. The Bayesian causal forest model permits treatment effect heterogeneity to be regularized separately from the prognostic effect of control variables, making it possible to informatively “shrink to homogeneity”. While we focus on observational data, our methods are equally useful for inferring heterogeneous treatment effects from randomized controlled experiments where careful regularization is somewhat less complicated but no less important.

Bio:
I am a professor of statistics at The University of Texas McCombs School of Business. My research focuses on Bayesian statistics in high-dimensional problems with applications ranging from finance to genetics. Some of my current projects include work on causal inference, machine learning, policy evaluation and empirical asset pricing. I am also the Executive Director of the Salem Center for Policy, a unit dedicated to support research, education, and dialogue around the impact of economic policies on markets and the free enterprise system.

Brian Ziebart, Associate Professor
Department of Computer Science, University of Illinois at Chicago

Abstract: TBA

Minjeong Jeon, Associate Professor
Department of Education, UCLA

Abstract: TBA

Guido Montufar, Assistant Professor
Departments of Mathematics and Statistics, UCLA

Abstract: TBA