Extension of Three-Variable Counterfactual Casual Graphic Model: from Two-Value to Three-Value Random Variable
Methodology
2012-07-02 v2 Artificial Intelligence
Abstract
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.
Keywords
Cite
@article{arxiv.1206.6570,
title = {Extension of Three-Variable Counterfactual Casual Graphic Model: from Two-Value to Three-Value Random Variable},
author = {Jingwei Liu},
journal= {arXiv preprint arXiv:1206.6570},
year = {2012}
}