Missing values constitute an important challenge in real-world machine learning for both prediction and causal discovery tasks. However, only few methods in causal discovery can handle missing data in an efficient way, while existing imputation methods are agnostic to causality. In this work we propose VICAUSE, a novel approach to simultaneously tackle missing value imputation and causal discovery efficiently with deep learning. Particularly, we propose a generative model with a structured latent space and a graph neural network-based architecture, scaling to large number of variables. Moreover, our method can discover relationship between groups of variables which is useful in many real-world applications. VICAUSE shows improved performance compared to popular and recent approaches in both missing value imputation and causal discovery.<\/p>\n","protected":false},"excerpt":{"rendered":"
Missing values constitute an important challenge in real-world machine learning for both prediction and causal discovery tasks. However, only few methods in causal discovery can handle missing data in an efficient way, while existing imputation methods are agnostic to causality. In this work we propose VICAUSE, a novel approach to simultaneously tackle missing value imputation 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