Scalable Causal Discovery with Score Matching
This work addresses the computational bottleneck in causal discovery for researchers and practitioners, offering a scalable solution that reduces complexity, though it is incremental as it builds on prior score-based methods.
The paper tackles the problem of discovering causal graphs from observational data in non-linear additive Gaussian noise models, achieving competitive accuracy with state-of-the-art methods while being over an order of magnitude faster.
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.