VICause: Simultaneous Missing Value Imputation and Causal Discovery with
Groups
- URL: http://arxiv.org/abs/2110.08223v1
- Date: Fri, 15 Oct 2021 17:35:20 GMT
- Title: VICause: Simultaneous Missing Value Imputation and Causal Discovery with
Groups
- Authors: Pablo Morales-Alvarez, Angus Lamb, Simon Woodhead, Simon Peyton Jones,
Miltiadis Allamanis, Cheng Zhang
- Abstract summary: We propose VICause, a novel approach to tackle missing value imputation and causal discovery efficiently with deep learning.
We show improved performance compared to popular and recent approaches in both missing value imputation and causal discovery.
- Score: 12.055670392677248
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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