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--- seminars:stat:mar212024 [2024/03/15 22:45] – qyu
+++ seminars:stat:mar212024 [2024/03/21 14:26] (current) – qyu
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+<WRAP centeralign>##Statistics Seminar##\\ Department of Mathematics and Statistics</WRAP>
+<WRAP 70% center>
+^  **DATE:**|Thursday, March 21, 2024 |
+^  **TIME:**|1:15pm -- 2:15pm |
+^  **LOCATION:**|WH 100E |
+^  **SPEAKER:**|Jingze Liu, Binghamton University |
+^  **TITLE:**|Continuous Treatment Effect Estimation via Generative Adversarial De-confounding  |
+</WRAP>
+\\
+<WRAP center box 80%>
+<WRAP centeralign>**Abstract**</WRAP>
+One fundamental problem in causal inference is the treatment
+effect estimation in obser- vational studies, and its key challenge is
+to handle the confounding bias induced by the associations between
+covariates and treatment variable. This paper study the prob- lem of
+effect estimation on continuous treatment from observational data, going
+beyond previous work on binary treatments. Previous work for binary
+treatment focuses on de- confounding by balancing the distribution of
+covariates between the treated and control groups with either propensity
+score or confounder balancing techniques. In the continuous setting,
+those methods would fail as we can hardly evaluate the distribution of
+covariates under each treatment status. To tackle the case of continuous
+treatments, this paper propose a novel Generative Adversarial
+De-confounding (GAD) algorithm to eliminate the associa- tions between
+covariates and treatment variable with two main steps: (1) generating an
+“calibration” distribution without associations between covariates and
+treatment by ran- dom perturbation; (2) learning sample weight that
+transfer the distribution of observed data to the “calibration”
+distribution for de-confounding with a Generative Adversarial Network.
+Extensive experiments on both synthetic and real-world datasets
+demonstrate that our algorithm outperforms the state-of-the-art methods
+for effect estimation of con- tinuous treatment with observational data.
+</WRAP>