do {simDAG}

Pearls do-operator for DAG objects

Description

This function can be used to set one or more nodes in a given DAG object to a specific value, which corresponds to an intervention on a DAG as defined by the do-operator introduced by Judea Pearl.

Usage

do(dag, names, values)

Arguments

dag

A DAG object created using the empty_dag and node functions. See ?node for more information on how to specify a DAG.

names

A character string specifying names of nodes in the dag object. The value of these nodes will be set to the corresponding value specified in the values argument. If the node is not already defined in dag, a new one will be added without warning.

values

A vector or list of any values. These nodes defined with the names argument will be set to those values.

Details

Internally this function simply removes the old node definition of all nodes in names and replaces it with a new node definition that defines the node as a constant value, irrespective of the original definition. The same effect can be created by directly specifying the DAG in this way from the start (see examples).

This function does not alter the original DAG in place. Instead, it returns a modified version of the DAG. In other words, using only do(dag, names="A", values=3) will not change the dag object.

Value

Returns a DAG object with updated node definitions.

Author(s)

Robin Denz

References

Judea Pearl (2009). Causality: Models, Reasoning and Inference. 2nd ed. Cambridge: Cambridge University Press

Examples

library(simDAG)

# define some initial DAG
dag <- empty_dag() +
  node("death", "binomial", c("age", "sex"), betas=c(1, 2), intercept=-10) +
  node("age", type="rnorm", mean=10, sd=2) +
  node("sex", parents="", type="rbernoulli", p=0.5) +
  node("smoking", parents=c("sex", "age"), type="binomial",
       betas=c(0.6, 0.2), intercept=-2)

# return new DAG with do(smoking = TRUE)
dag2 <- do(dag, names="smoking", values=TRUE)

# which is equivalent to
dag2 <- empty_dag() +
  node("death", "binomial", c("age", "sex"), betas=c(1, 2), intercept=-10) +
  node("age", type="rnorm", mean=10, sd=2) +
  node("sex", parents="", type="rbernoulli", p=0.5) +
  node("smoking", type="rconstant", constant=TRUE)

# use do() on multiple variables: do(smoking = TRUE, sex = FALSE)
dag2 <- do(dag, names=c("smoking", "sex"), values=list(TRUE, FALSE))