Ordering-based Causal Discovery via Generalized Score Matching
This work addresses causal discovery from discrete data, an incremental advance in a domain-specific problem.
The paper tackled the challenge of learning DAG structures from observational data by extending score matching to discrete data, introducing a novel leaf discriminant criterion, and demonstrated accurate inference of causal orders that boosted baseline accuracy in experiments.
Learning DAG structures from purely observational data remains a long-standing challenge across scientific domains. An emerging line of research leverages the score of the data distribution to initially identify a topological order of the underlying DAG via leaf node detection and subsequently performs edge pruning for graph recovery. This paper extends the score matching framework for causal discovery, which is originally designated for continuous data, and introduces a novel leaf discriminant criterion based on the discrete score function. Through simulated and real-world experiments, we demonstrate that our theory enables accurate inference of true causal orders from observed discrete data and the identified ordering can significantly boost the accuracy of existing causal discovery baselines on nearly all of the settings.