causaloptim (version 1.0.0)

create_causalmodel: Create a structural causal model from a graph or a set of response functions

Description

Given either a graph or a set of response functions (i.e., either graph or respvars may be provided), and a specification of what conditional probabilities are observed, produce a causal model.

Usage

create_causalmodel(
  graph = NULL,
  respvars = NULL,
  prob.form,
  p.vals,
  constraints = NULL,
  right.vars = NULL
)

Value

An object of class "causalmodel"

Arguments

graph

A graph with special edge and vertex attributes, as produced by initialize_graph

respvars

List of response functions as produced by create_response_function

prob.form

A list with two named elements "out", "cond" where each element is a character vector of variable names that appear in p.vals

p.vals

Data frame defining which probabilities are observable. The variable names of p.vals must all appear in prob.form. If missing, will assume that all combinations of the variables values are observed.

constraints

A vector of character strings that represent the constraints on counterfactual quantities

right.vars

A vector of character strings indicating which variables are on the right side of the graph. Only required when graph is NULL. See examples.

Details

It is assumed that probabilities of the form p(out | cond) are observed, for each combination of values in p.vals. cond may be NULL in which case nothing is conditioned on.

The constraints are specified in terms of potential outcomes to constrain by writing the potential outcomes, values of their parents, and operators that determine the constraint (equalities or inequalities). For example, X(Z = 1) >= X(Z = 0)

Examples

 ## regular IV case

graph <- initialize_graph(graph_from_literal(Z -+ X, X -+ Y, Ur -+ X, Ur -+ Y))

iv_model <- create_causalmodel(graph, prob.form = list(out = c("X", "Y"), cond = "Z"))
# with monotonicity
iv_model_mono <- create_causalmodel(graph, prob.form = list(out = c("X", "Y"), cond = "Z"),
                 constraints = list("X(Z = 1) >= X(Z = 0)"))
                 
#showing the use of right.vars
b <- initialize_graph(graph_from_literal(Ul -+ X -+ Y -+ Y2, Ur -+ Y, Ur -+ Y2))
V(b)$latent <- c(1, 0, 1, 0, 1)
respvars <- create_response_function(b)
create_causalmodel(graph = b, constraints = "Y2(Y = 1) >= Y2(Y = 0)",
                   p.vals = expand.grid(X = 0:1, Y2 = 0:1), 
                   prob.form = list(out = "Y2", cond = "X"))
 ## need to specify right.vars because it cannot be inferred from the response functions alone
create_causalmodel(graph = NULL, respvars = respvars, 
                   constraints = "Y2(Y = 1) >= Y2(Y = 0)",
                   p.vals = expand.grid(X = 0:1, Y2 = 0:1), 
                   prob.form = list(out = "Y2", cond = "X"), 
                   right.vars = c("Y", "Y2"))