2021
Deep Generative Missingness Pattern-Set Mixture Models
AISTATS 2021poster
We propose a variational autoencoder architecture to model both ignorable and nonignorable missing data using pattern-set mixtures as proposed by Little (1993). Our model explicitly learns to cluster the missing data into missingness pattern sets based on the observed data and missingness masks. Und…