Causality plays a pivotal role in various fields of study. Based on the framework of causal graphical models, previous works have proposed identifying whether a variable is a cause or non-cause of a target in every Markov equivalent graph solely by learning a local structure. However, the presence of prior knowledge, often represented as a partially known causal graph, is common in many causal modeling applications. Leveraging this prior knowledge allows for the further identification of causal relationships. In this paper, we first propose a method for learning the local structure using all types of causal background knowledge, including direct causal information, non-ancestral information and ancestral information. Then we introduce criteria for identifying causal relationships based solely on the local structure in the presence of prior knowledge. We also apply out method to fair machine learning, and experiments involving local structure learning, causal relationship identification, and fair machine learning demonstrate that our method is both effective and efficient.

本文研究了如何在局部结构学习中有效利用先验知识，以识别因果关系。在众多因果建模应用中，常常存在部分已知的因果图，研究提出了一种结合直接因果信息、非祖先信息和祖先信息的方法，以提升因果识别的准确性和效率。本研究的关键发现是，通过这种新方法可以提高局部结构学习和公平机器学习的效果。 

带背景知识的局部因果发现