Causal Machine Learning: Regularization, Confounding, & Heterogeneous effects

On March 12, 2021 the Center on Technology, Data and Society is hosting a talk with Prof. Richard Hahn, who will discuss the latest developments on using machine learning to identify heterogeneous treatment effects. Details below: 

Abstract

In this talk I will describe recent work on supervised machine learning for conditional average treatment effects. I will motivate the proposed 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 practical discussion of designing simulation studies to systematically investigate and validate machine learning models for causal inference. 

Related Demo: https://math.la.asu.edu/~prhahn/xbcf_demo.html

Bio

Professor 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, procedural mysteries (novels, TV, or movies), and riding and working on bicycles.