Missing values constitute an important challenge in real-world machine learning for both prediction and causal discovery tasks. However, existing imputation methods are agnostic to causality, while only few methods in traditional causal discovery can handle missing data in an efficient way. 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 relationships 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.

翻译：缺失的值是真实世界机器学习预测和因果发现任务的一个重要挑战。 但是,现有的估算方法对因果关系具有不可知性,而传统因果发现中只有很少的方法能够有效地处理缺失的数据。 在这项工作中,我们提议了一种新颖的方法,即与深层学习同时有效处理缺失的值估算和因果发现。特别是,我们提出了一个具有结构化潜在空间的基因化模型和一个基于图表神经网络的架构,将范围扩大到大量变量。此外,我们的方法可以发现在很多现实世界应用中有用的各种变量之间的关系。 与常见和近期方法相比,在缺失的值估算和因果发现方面表现有所改善。