CUTS: Neural Causal Discovery from Irregular Time-Series Data

Understand

Causal discovery from time-series data has been a central task in machine learning.

• Recently, Granger causality inference is gaining momentum due to its good explainability and high compatibility with emerging deep neural networks.
• However, most existing methods assume structured input data and degenerate greatly when encountering data with randomly missing entries or non-uniform sampling frequencies, which hampers their applications in real scenarios.
• To address this issue, here we present CUTS, a neural Granger causal discovery algorithm to jointly impute unobserved data points and build causal graphs, via plugging in two mutually boosting modules in an iterative framework: (i) Latent data prediction stage: designs a Delayed Supervision Graph Neural Network (DSGNN) to hallucinate and register unstructured data which might be of high dimension and with complex distribution; (ii) Causal graph fitting stage: builds a causal adjacency matrix with imputed data under sparse penalty.