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Session Submission Type: Created Panel
These papers explore techniques for dealing with missing data and data augmentation.
Model Free Multiple Imputation for Missing Data - Jason Anastasopoulos, University of Georgia; Sarah Hunter, University of Colorado, Boulder; Keith T. Poole, University of Georgia
The Tower Method: a Novel Approach to Missingness - Pete Mohanty, Stanford University; Norman Matloff
Using Machine Learning Predictions in Linear Models - Matthew Tyler, Stanford University
Augmenting Political Data through Generative Adversarial Networks (GANs) - Budrul Ahsan, Philips Japan; Sota Kato, International University of Japan; Takafumi Nakanishi, Musashino University; Hirokazu Shimauchi, The Tokyo Foundation for Policy Research