Causal Discovery Evaluation Framework in the Absence of Ground-Truth Causal Graph

Causal Discovery Evaluation Framework in the Absence of Ground-Truth Causal Graph

In causal learning, discovering the causal graph of the underlying generative mechanism from observed data is crucial. However, real-world data for causal discovery is scarce and expensive, leading researchers to rely on synthetic datasets, which may not accurately reflect real-world performance. To...

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Bibliographic Details
Main Authors: Tingpeng Li, Lei Wang, Danhua Peng, Jun Liao, Li Liu, Zhendong Liu
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Causal discovery
Markov blanket
causal graphical models
cause effect identification
condition independence testing
Online Access:https://ieeexplore.ieee.org/document/10669554/

Internet

https://ieeexplore.ieee.org/document/10669554/

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