Towards Privacy-Aware Causal Structure Learning in Federated Setting

Adaptive Skeleton Construction for Accurate DAG Learning

FedCSL: A Scalable and Accurate Approach to Federated Causal Structure Learning

DAGAF is a novel generative framework that simultaneously discovers causal structures and produces high-fidelity synthetic tabular data.

Bootstrap-based Causal Structure Learning

Sample Quality Heterogeneity-aware Federated Causal Discovery through Adaptive Variable Space Selection

Progressive Skeleton Learning for Effective Local-to-Global Causal Structure Learning

Enhancing Causal Discovery in Federated Settings with Limited Local Samples