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--- seminars:stat:mar282024 [2024/03/19 10:51] – qyu
+++ seminars:stat:mar282024 [2024/03/19 10:54] (current) – qyu
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+<WRAP centeralign>##Statistics Seminar##\\ Department of Mathematics and Statistics</WRAP>
+<WRAP 70% center>
+^  **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  |
+</WRAP>
+\\
+<WRAP center box 80%>
+<WRAP centeralign>**Abstract**</WRAP>
+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.
+</WRAP>