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考虑一个多变量联合概率分布<math>P_{X}</math>和一个有向无环图DAG <math>\mathcal{G}</math>.

考虑一个多变量联合概率分布<math>P_{X}</math>和一个有向无环图DAG <math>\mathcal{G}</math>.

定义：联合概率分布 <math>P_{X}</math> 对于给定的 DAG  <math>\mathcal{G}</math> 满足因果忠实性，如果<ref name="Elements">Peters Jonas,Janzing Dominik,Schlkopf Bernhard (2017) [https://pattern.swarma.org/paper?id=5c93b918-c3ba-11eb-8fd5-0242ac170007 Elements of Causal Inference: Foundations and Learning Algorithms].</ref>：

定义：联合概率分布 <math>P_{X}</math> 对于给定的 [[DAG]] <math>\mathcal{G}</math> 满足因果忠实性，如果<ref name="Elements">Peters Jonas,Janzing Dominik,Schlkopf Bernhard (2017) [https://pattern.swarma.org/paper?id=5c93b918-c3ba-11eb-8fd5-0242ac170007 Elements of Causal Inference: Foundations and Learning Algorithms].</ref>：

<math>A \perp\!\!\!\perp B \mid C \Rightarrow A \perp\!\!\!\perp_{\mathcal{G}} B \mid C</math>

<math>A \perp\!\!\!\perp B \mid C \Rightarrow A \perp\!\!\!\perp_{\mathcal{G}} B \mid C</math>

==参考文献==

==参考文献==

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