seminars:stat:dec052024 [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, December 05, 2024 |
^  **TIME:**|1:15pm -- 2:15pm |
^  **LOCATION:**|WH 100E |
^  **SPEAKER:**|Baozhen Wang, Binghamton University |
^  **TITLE:**|Conformal Prediction Methods for Distribution Shifts and Causal Effect Estimation  |
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<WRAP centeralign>**Abstract**</WRAP>
Conformal prediction is a distribution-free framework to
quantify uncertainty by generating prediction sets that contain the
true response with a user-specified probability. Conformal prediction
is highly valuable across domains, as it does not rely on assumptions
about the underlying data distribution, ensuring reliable predictions
even in complex, real-world applications. Despite its strengths,
conformal prediction faces several limitations that restrict its
utility in practical settings: it relies on the exchangeability
between training and test data, which limits its effectiveness under
distribution shifts; the prediction efficiency depends on the choice
of score functions, and current methods can yield conservative
prediction intervals that hinder practical usability. We aim to
address these limitations from three aspects, extending conformal
prediction to broader settings with enhanced efficiency and
reliability.

In the first work, we develop a weighted conformal classification
method for covariate shift with posterior drift (CSPD), where both
feature distributions and conditional distribution of labels given
features differ between the source and the target data. The second
work studies conformal prediction for causal inference. By choosing
the conditional densities as the score function, we utilize a
reference distribution technique to produce valid, narrower prediction
intervals for individual treatment effects (ITE), improving
state-of-the-art methods. Finally, the third work introduces a
penalized approach to conformal prediction in regression, optimizing
interval length while maintaining coverage guarantees.
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