Extension of Three-Variable Counterfactual Casual Graphic Model: from Two-Value to Three-Value Random Variable
classification
📊 stat.ME
cs.AI
keywords
distributioncounterfactualextensiongraphicthree-valuevariablescausalmodel
pith:4QKEP26L Add to your LaTeX paper
What is a Pith Number?\usepackage{pith}
\pithnumber{4QKEP26L}
Prints a linked pith:4QKEP26L badge after your title and writes the identifier into PDF metadata. Compiles on arXiv with no extra files. Learn more
read the original 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.
This paper has not been read by Pith yet.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.