The Statistical Learning and Data Science Section presents its March webinar by Dr. Jason Klusowski. It is scheduled on March 30, 2023. Hope to see you then.
Title: Pointwise Behavior of Recursive Partitioning and its Implications for Heterogeneous Causal Effect Estimation
Speakers: Dr. Jason Klusowski, Department of Operations Research and Financial Engineering, Princeton University
Date and Time: March 30, 2023, 2:00 to 3:30 pm Eastern Time
Registration Link: ASA SLDS Webinar Registration Link [eventbrite.com]
Abstract: Decision tree learning is increasingly being used for pointwise inference. Important applications include causal heterogenous treatment effects and dynamic policy decisions, as well as conditional quantile regression and design of experiments, where tree estimation and inference is conducted at specific values of the covariates. In this talk, I call into question the use of decision trees (trained by adaptive recursive partitioning) for such purposes by demonstrating that they can fail to achieve polynomial rates of convergence in uniform norm, even with pruning. Instead, the convergence may be poly-logarithmic or, in some important special cases, such as honest regression trees, fail completely. This talk is based on joint work with Matias Cattaneo and Peter Tian.
Presenter: Jason Klusowski is an assistant professor in the Department of Operations Research and Financial Engineering (ORFE) at Princeton University. Prior to joining Princeton, he was an assistant professor in the Department of Statistics at Rutgers University, New Brunswick. He received his PhD in Statistics and Data Science from Yale University in 2018. His research explores the tradeoffs among interpretability, statistical accuracy, and computational feasibility in learning algorithms. He is a recipient of the National Science Foundation CAREER Award.
Zhihua Su, PhD
Department of Statistics
University of Floridazhihuasu@stat.ufl.edu