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

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^  **DATE:**|Thursday, September 21, 2023 |
^  **TIME:**|1:15pm -- 2:15pm |
^  **LOCATION:**|WH 100E |
^  **SPEAKER:**|Xinhai Zhang, Binghamton University |
^  **TITLE:**|Proximal Learning for Individualized Treatment Regimes Under Unmeasured Confounding  |
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<WRAP centeralign>**Abstract**</WRAP>
Data-driven individualized decision making has recently received
increasing research interest. However, most existing methods rely on the assumption
of no unmeasured confounding, which cannot be ensured in practice especially in
observational studies. Motivated by the recently proposed proximal causal inference,
there has been some development on estimating optimal individualized treatment
regimes (ITRs) in the presence of unmeasured confounding. Explicitly, in terms of
two types of proxy variables, we are able to establish several identification
results for different classes of ITRs respectively, exhibiting the trade-off between
the risk of making untestable assumptions and the potential improvement of the value
function in decision making. Based on these identification results,
classification-based approaches can be applied to find a variety of restricted
in-class optimal ITRs.
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