Mitigating Prior Errors in Causal Structure Learning: A Resilient Approach via Bayesian Networks.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Causal Inference

Background:

• Causal structure learning (CSL) uses Bayesian Networks (BNs) to model cause-and-effect relationships.
• Integrating prior knowledge enhances CSL but is sensitive to prior errors.
• Existing methods struggle with prior inaccuracies, often requiring expert input.

Purpose of the Study:

• To propose a novel CSL strategy robust to edge-level prior knowledge errors.
• To minimize human intervention in CSL by addressing prior inaccuracies.
• To enhance the quality and reliability of Bayesian Network learning.

Main Methods:

• Classifying types of prior errors and analyzing their theoretical impact on Structural Hamming Distance (SHD).
• Identifying a unique acyclic structure, termed "quasi-circle," linked to significant prior error impact.
• Developing a post-hoc strategy to detect prior errors based on their contribution to "quasi-circles."

Main Results:

• Demonstrated robustness against various prior errors on real and synthetic datasets.
• Quantified the theoretical impact of different prior errors on SHD.
• Showcased significant resistance to order-reversed errors while preserving correct prior information.

Conclusions:

• The proposed strategy effectively enhances causal structure learning in the presence of prior errors.
• The "quasi-circle" concept provides a novel mechanism for identifying and mitigating prior inaccuracies.
• This approach offers a more reliable and less intervention-dependent method for Bayesian Network structure learning.