Extension of Three-Variable Counterfactual Casual Graphic Model: from   Two-Value to Three-Value Random Variable
  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. 
Jingwei Liu
stat.ME cs.AI