Extension of Three-Variable Counterfactual Casual Graphic Model: from Two-Value to Three-Value Random Variable
This work is incremental, addressing a specific extension in causal inference for theoretical research.
The paper tackles the extension of a three-variable counterfactual causal graphical model from binary to ternary random variables, deriving sufficient conditions for identifiability using conditional independence as ancillary information.
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.