aiqbench takeAI Research

This research advances causal discovery by extending score matching frameworks to discrete data through a novel leaf discriminant criterion. The method effectively infers topological orders and significantly improves graph recovery accuracy across diverse settings compared to existing baselines.

Source coveringarXiv

What this isA model's reading of the article and related coverage, written for aiqbench. Not the publisher's words.

Ordering-based Causal Discovery via Generalized Score Matching

arXivJuly 28, 20262 min read

arXiv:2601.16249v3 Announce Type: replace-cross Abstract: 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.

Abstract: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.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2601.16249 [cs.LG]

(or arXiv:2601.16249v3 [cs.LG] for this version)

Full text under the publisher's open licence · 133 words

Read at arXiv ↗Ask about this article