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Statistics Seminar
Department of Mathematics and Statistics

DATE:Thursday, March 28, 2024
TIME:1:15pm – 2:15pm
SPEAKER:Baozhen Wang, Binghamton University
TITLE:Conformal Meta-learners for Predictive Inference of Individual Treatment Effects


This paper investigates predictive inference for individual treatment effects (ITEs) using machine learning techniques. Traditional approaches have primarily concentrated on developing meta-learners for estimating the conditional average treatment effect (CATE), offering point estimates without considering predictive intervals. The study introduces conformal meta-learners, a framework that enhances traditional CATE meta-learners by applying the conformal prediction procedure to provide predictive intervals for ITEs. This method is validated through a stochastic ordering framework, highlighting that conformal meta-learners can achieve valid inferences with desired coverage levels.

seminars/stat/mar282024.txt · Last modified: 2024/03/19 06:54 by qyu