seminars:stat:mar282024 [Department of Mathematics and Statistics, Binghamton University]

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<WRAP centeralign>##Statistics Seminar##\\ Department of Mathematics and Statistics</WRAP>

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^  **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  |
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<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.
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