##Statistics Seminar##\\ Department of Mathematics and Statistics ^ **DATE:**|Thursday, March 28, 2024 | ^ **TIME:**|1:15pm -- 2:15pm | ^ **LOCATION:**|WH 100E | ^ **SPEAKER:**|Baozhen Wang, Binghamton University | ^ **TITLE:**|Conformal Meta-learners for Predictive Inference of Individual Treatment Effects | \\ **Abstract** 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.