fairness {R6causal}

Checking fairness of a prediction via counterfactual simulation

Description

Checking fairness of a prediction via counterfactual simulation

Usage

fairness(
  modellist,
  scm,
  sensitive,
  condition,
  condition_type,
  parents,
  n,
  sens_values = NULL,
  modeltype = "predict",
  method,
  control = NULL,
  ...
)

Arguments

modellist

A list of model objects that have a predict method or a list of functions that return predictions

scm

An SCM object

sensitive

A character vector of the names of sensitive variables

condition

A data.table consisting of the valid rows ( e.g. data.table::data.table( x = 0, y = 0))

condition_type

(required only if method == "u_find") A character vector giving the type ("continuous" or "discrete") of every variable in condition

parents

A character vector of the names of variables that remain fixed

n

The number of rows in the data to be simulated by counterfactual

sens_values

A data.table specifying the combinations of the values of sensitive variables to be considered (default NULL meaning the all possible combinations of the values of sensitive variables)

modeltype

"predict" (default) or "function" depending on the type modellist

method

The simulation method, "u_find", "rejection" or "analytic_linear_gaussian"

control

List of parameters to be passed to the simulation method, see counterfactual.

...

Other arguments passed to predict or to the prediction functions.

Value

A list containing a data table for element of modellist. Each data table contains the predicted values after counterfactual interventions on the sensitive variables.

Examples

trainingd <- backdoor$simulate(10000, return_simdata = TRUE)
newd <- backdoor$simulate(100, return_simdata = TRUE)
vnames <- backdoor$vnames
m1 <- lm(y ~  x + z, data = trainingd)
m2 <- lm(y ~  z, data = trainingd)
fairlist <- fairness(modellist = list(m1,m2),
                     scm = backdoor,
                     sensitive = c("x"),
                     sens_values = data.table::data.table(x=c(0,1)),
                     condition = newd[1,c("x","y")],
                     condition_type = list(x = "cont",
                                           z = "cont",
                                           y = "cont"),
                     parents = NULL,
                     n = 20,
                     modeltype = "predict",
                     method = "u_find")