  Missing data imputation is a fundamental problem in data analysis, and many
studies have been conducted to improve its performance by exploring model
structures and learning procedures. However, data augmentation, as a simple yet
effective method, has not received enough attention in this area. In this
paper, we propose a novel data augmentation method called Missingness
Augmentation (MisA) for generative imputation models. Our approach dynamically
produces incomplete samples at each epoch by utilizing the generator's output,
constraining the augmented samples using a simple reconstruction loss, and
combining this loss with the original loss to form the final optimization
objective. As a general augmentation technique, MisA can be easily integrated
into generative imputation frameworks, providing a simple yet effective way to
enhance their performance. Experimental results demonstrate that MisA
significantly improves the performance of many recently proposed generative
imputation models on a variety of tabular and image datasets. The code is
available at \url{https://github.com/WYu-Feng/Missingness-Augmentation}.
