Extension of Three-Variable Counterfactual Casual Graphic Model: from Two-Value to Three-Value Random Variable

06/28/2012

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by   Jingwei Liu, et al.

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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.