Local Causal Discovery With Background Knowledge.

Area of Science:

• Causal inference and machine learning.

Background:

• Causal graphical models are crucial for understanding cause-and-effect relationships.
• Prior knowledge, such as partially known causal graphs, is often available in real-world applications.
• Existing methods focus on learning local structure but may not fully leverage prior knowledge.

Purpose of the Study:

• To develop a method for learning local structure in causal graphical models by incorporating various types of causal background knowledge.
• To establish sufficient and necessary conditions for identifying causal relations using local structure and prior knowledge.
• To demonstrate the effectiveness and efficiency of the proposed methods.

Main Methods:

• Incorporating direct causal, non-ancestral, and ancestral information into local structure learning.
• Developing criteria for causal relation identification based on local structure and prior knowledge.
• Experimental validation on synthetic and real-world datasets.

Main Results:

• The proposed method effectively learns local structure by integrating background knowledge.
• Sufficient and necessary conditions for causal identification were successfully derived.
• The approach demonstrated efficiency in causal relation identification and fair machine learning applications.

Conclusions:

• Integrating prior causal knowledge significantly improves the learning of local structure and causal identification.
• The developed conditions provide a robust framework for causal inference with background knowledge.
• The method has practical implications for advancing fair machine learning and other causal modeling applications.