MutilateGraph: Mutilate graph

In cia: Learn and Apply Directed Acyclic Graphs for Causal Inference

Mutilate graph

Description

Mutilate a graph in accordance with an intervention. This is typically used to perform a do-operation on a given graph. Please note that any evidence set within the original grain object will not be passed to the new object.

Usage

MutilateGraph(grain_object, intervention)

Arguments

grain_object	A grain object.
intervention	A list of nodes and their corresponding intervention distribution represented as a vector of unconditional probabilities.

Value

A grain object.

Examples

# This creates a mutilated graph in accordance with turning the sprinkler 
# on in the wet grass example (i.e, do(S = 'yes')).
yn <- c("yes", "no")
p.R <- gRain::cptable(~R, values=c(.2, .8), levels=yn)
p.S_R <- gRain::cptable(~S:R, values=c(.01, .99, .4, .6), levels=yn)
p.G_SR <- gRain::cptable(~G:S:R, values=c(.99, .01, .8, .2, .9, .1, 0, 1), levels=yn)
wet.cpt <- gRain::grain(gRain::compileCPT(p.R, p.S_R, p.G_SR))

mut_graph <- MutilateGraph(wet.cpt, list(S = c(1.0, 0.0)))

# You can then use querygrain to perform an intervention query. For example,
# p(G | do(S = 'yes')) is given by,
gRain::querygrain(mut_graph, 'G')

# You can also perform an observational query for a node not affected
# by the intervention. For example, p(R | do(S = 'yes')) is given by,
gRain::querygrain(mut_graph, 'R')

cia documentation built on April 4, 2025, 5:23 a.m.