FedCSL: A Scalable and Accurate Approach to Federated Causal Structure Learning
As an emerging research direction, federated causal structure learning (CSL) aims at
learning causal relationships from decentralized data across multiple clients while …
learning causal relationships from decentralized data across multiple clients while …
A novel data enhancement approach to DAG learning with small data samples
Learning a directed acyclic graph (DAG) from observational data plays a crucial role in
causal inference and machine learning. However, the scarcity of observational data is a …
causal inference and machine learning. However, the scarcity of observational data is a …
An efficient skeleton learning approach-based hybrid algorithm for identifying Bayesian network structure
N Wang, H Liu, L Zhang, Y Cai, Q Shi - Engineering Applications of …, 2024 - Elsevier
Bayesian network (BN) structure learning is the basis of BN applications and plays a pivotal
role in many machine learning tasks. Whereas remarkable progress in structure learning …
role in many machine learning tasks. Whereas remarkable progress in structure learning …
Loose-to-strict Markov blanket learning algorithm for feature selection
N Wang, H Liu, L Zhang, Y Cai, Q Shi - Knowledge-Based Systems, 2024 - Elsevier
The Markov blanket (MB) represents a crucial concept in a Bayesian network (BN) and is
theoretically the optimal solution to the feature selection problem. Methods based on …
theoretically the optimal solution to the feature selection problem. Methods based on …
Local causal structure learning with missing data
Local causal structure learning aims to discover and distinguish the direct causes and direct
effects of a target variable. However, the state-of-the-art algorithms for local causal structure …
effects of a target variable. However, the state-of-the-art algorithms for local causal structure …
Bootstrap-based Layer-wise Refining for Causal Structure Learning
Learning causal structures from observational data is critical for causal discovery and many
machine learning tasks. Traditional constraint-based methods first adopt conditional …
machine learning tasks. Traditional constraint-based methods first adopt conditional …
Towards privacy-aware causal structure learning in federated setting
Causal structure learning has been extensively studied and widely used in machine
learning and various applications. To achieve an ideal performance, existing causal …
learning and various applications. To achieve an ideal performance, existing causal …
Causal Discovery Using Weight-Based Conditional Independence Test
Conditional independence (CI) tests play an essential role in causal discovery from
observational data, enabling the measurement of independence between two nodes …
observational data, enabling the measurement of independence between two nodes …
[PDF][PDF] Sample Quality Heterogeneity-aware Federated Causal Discovery through Adaptive Variable Space Selection
Federated causal discovery (FCD) aims to uncover causal relationships among variables
from decentralized data across multiple clients, while preserving data privacy. In practice …
from decentralized data across multiple clients, while preserving data privacy. In practice …