Arrow is a pretrained transformer that discovers causal graphs from observational data by factorizing them into skeletons and orders, trained end-to-end on diverse synthetic examples to match or exceed prior methods at lower inference cost.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
method 1
citation-polarity summary
fields
cs.LG 2years
2026 2roles
method 1polarities
use method 1representative citing papers
A continuous mixed-effects DAG estimator jointly recovers fixed and random edge structure on clustered data, with identifiability and asymptotic structure-recovery guarantees.
citing papers explorer
-
Arrow: A Foundation Model for Causal Discovery
Arrow is a pretrained transformer that discovers causal graphs from observational data by factorizing them into skeletons and orders, trained end-to-end on diverse synthetic examples to match or exceed prior methods at lower inference cost.
-
Structure Learning on Clustered Data
A continuous mixed-effects DAG estimator jointly recovers fixed and random edge structure on clustered data, with identifiability and asymptotic structure-recovery guarantees.