seminars:stat:may12025 [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, May 1, 2025 |
^  **TIME:**|1:15pm -- 2:15pm |
^  **LOCATION:**|WH 100E |
^  **SPEAKER:**|Xinhai Zhang, Binghamton University |
^  **TITLE:**|Neural Network Models in CATE Estimation |
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<WRAP centeralign>**Abstract**</WRAP>
Estimating heterogeneous treatment effects has become increasingly important across empirical sciences. In this talk, I will present an overview of how neural network models have been applied to conditional average treatment effect (CATE) estimation. Motivated by the meta-learning framework, where treatment effect estimation is broken down into supervised learning sub-tasks, I will summarize key strategies based on plug-in estimation and pseudo-outcome regression. I will discuss how different neural network architectures have been designed or adapted for these strategies, and highlight their relative strengths and weaknesses. Throughout, I will connect theoretical insights to practical considerations, drawing on simulation studies that compare model performance under varying data-generating conditions. The goal of the talk is to provide a structured understanding of the current landscape of neural network-based approaches to CATE estimation.</WRAP>