verma.constraints: Find Verma constraints for a given graph

In causaleffect: Deriving Expressions of Joint Interventional Distributions and Transport Formulas in Causal Models

Find Verma constraints for a given graph

Description

This functions computes functional constraints known as Verma constraints for a joint distribution of a given semi-Markovian causal model.

Usage

verma.constraints(G)

Arguments

G	An igraph object describing the directed acyclic graph induced by the causal model that matches the internal syntax.

Value

A list of lists, each with five components corresponding to the functional constraint. The two equal c-factors that imply the functional independence are described by lhs.cfactor and rhs.cfactor and their expressions are given by lhs.expr and rhs.expr respectively. The independent variables are given by vars.

Author(s)

Santtu Tikka

References

Tian, J., Pearl J. 2002 On Testable Implications of Causal Models with Hidden variables. Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence, 519–527.

causaleffect documentation built on July 14, 2022, 5:07 p.m.