4 papers
Arrow: A Foundation Model for Causal Discovery
Ryan Thompson, He Zhao, Daniel M. Steinberg +1
We introduce Arrow, a foundation model for zero-shot causal discovery on observational tabular data. Arrow factorizes a directed acyclic graph into an undirected skeleton and a top…
Ordering-based Causal Discovery via Generalized Score Matching
Vy Vo, He Zhao, Trung Le +2
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 dis…
Rényi Neural Processes
Xuesong Wang, He Zhao, Edwin V. Bonilla
Neural Processes (NPs) are deep probabilistic models that represent stochastic processes by conditioning their prior distributions on a set of context points. Despite their advanta…
ProDAG: Projected Variational Inference for Directed Acyclic Graphs
Ryan Thompson, Edwin V. Bonilla, Robert Kohn
Directed acyclic graph (DAG) learning is a central task in structure discovery and causal inference. Although the field has witnessed remarkable advances over the past few years, i…