Scalable Causal Discovery with Score Matching

TL;DR

This paper introduces DAS, a scalable algorithm for causal graph discovery from observational data using score matching, achieving high accuracy with significantly reduced computational complexity.

Contribution

It extends score-based causal discovery to full graph recovery, reducing pruning complexity and enabling scalable, accurate causal inference.

Findings

DAS reduces pruning complexity proportionally to graph size.

DAS achieves competitive accuracy with state-of-the-art methods.

DAS is over ten times faster than existing approaches.

Abstract

This paper demonstrates how to discover the whole causal graph from the second derivative of the log-likelihood in observational non-linear additive Gaussian noise models. Leveraging scalable machine learning approaches to approximate the score function $\nabla \log p(\mathbf{X})$, we extend the work of Rolland et al. (2022) that only recovers the topological order from the score and requires an expensive pruning step removing spurious edges among those admitted by the ordering. Our analysis leads to DAS (acronym for Discovery At Scale), a practical algorithm that reduces the complexity of the pruning by a factor proportional to the graph size. In practice, DAS achieves competitive accuracy with current state-of-the-art while being over an order of magnitude faster. Overall, our approach enables principled and scalable causal discovery, significantly lowering the compute bar.