not-MIWAE: Deep Generative Modelling with Missing not at Random Data

Understand

When a missing process depends on the missing values themselves, it needs to be explicitly modelled and taken into account while doing likelihood-based inference.

• We present an approach for building and fitting deep latent variable models (DLVMs) in cases where the missing process is dependent on the missing data.
• Specifically, a deep neural network enables us to flexibly model the conditional distribution of the missingness pattern given the data.
• This allows for incorporating prior information about the type of missingness (e.g.