三变量反事实因果图模型的扩展：从二值到三值随机变量
Extension of Three-Variable Counterfactual Casual Graphic Model: from Two-Value to Three-Value Random Variable
摘要：
通过将有向无环图（DAG）中涉及的变量的二值分布推广到三值分布，讨论了有向无环图（DAG）中顶点集为三个变量的反事实因果图模型的推广。利用条件独立性作为辅助信息，将6种含变量的可拓反事实因果图模型从二值分布推广到三值分布，并给出了可辨识的充分条件.
英文摘要：
The extension of counterfactual causal graphic model with three variables of vertex set in directed acyclic graph (DAG) is discussed in this paper by extending two- value distribution to three-value distribution of the variables involved in DAG. Using the conditional independence as ancillary information, 6 kinds of extension counterfactual causal graphic models with some variables are extended from two-value distribution to three-value distribution and the sufficient conditions of identifiability are derived.
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