eval_cond_stats_parity {fairmetrics}R Documentation

Examine Conditional Statistical Parity of a Model

Description

This function evaluates conditional statistical parity, which measures fairness by comparing positive prediction rates across sensitive groups within a defined subgroup of the population. This is useful in scenarios where fairness should be evaluated in a more context-specific way—e.g., within a particular hospital unit or age bracket. Conditional statistical parity is a refinement of standard statistical parity. Instead of comparing prediction rates across groups in the entire dataset, it restricts the comparison to a specified subset of the population, defined by a conditioning variable.

Usage

eval_cond_stats_parity(
  data,
  outcome,
  group,
  group2,
  condition,
  probs,
  confint = TRUE,
  cutoff = 0.5,
  bootstraps = 2500,
  alpha = 0.05,
  message = TRUE,
  digits = 2
)

Arguments

data

Data frame containing the outcome, predicted outcome, and sensitive attribute

outcome

Name of the outcome variable, it must be binary

group

Name of the sensitive attribute

group2

Name of the group to condition on

condition

If the conditional group is categorical, the condition supplied must be a character of the levels to condition on. If the conditional group is continuous, the conditions supplied must be a character containing the sign of the condition and the value to threshold the continuous variable (e.g. "<50", ">50", "<=50", ">=50").

probs

Name of the predicted outcome variable

confint

Whether to compute 95% confidence interval, default is TRUE

cutoff

Threshold for the predicted outcome, default is 0.5

bootstraps

Number of bootstrap samples, default is 2500

alpha

The 1 - significance level for the confidence interval, default is 0.05

message

Logical; if TRUE (default), prints a textual summary of the fairness evaluation. Only works if confint is TRUE.

digits

Number of digits to round the results to, default is 2

Details

The function supports both categorical and continuous conditioning variables. For continuous variables, you can supply a threshold expression like "<50" or ">=75" to the condition parameter.

Value

A list containing the following elements:

See Also

eval_stats_parity

Examples


library(fairmetrics)
library(dplyr)
library(magrittr)
library(randomForest)
data("mimic_preprocessed")
set.seed(123)
train_data <- mimic_preprocessed %>%
  dplyr::filter(dplyr::row_number() <= 700)
# Fit a random forest model
rf_model <- randomForest::randomForest(factor(day_28_flg) ~ ., data = train_data, ntree = 1000)
# Test the model on the remaining data
test_data <- mimic_preprocessed %>%
  dplyr::mutate(gender = ifelse(gender_num == 1, "Male", "Female")) %>%
  dplyr::filter(dplyr::row_number() > 700)

test_data$pred <- predict(rf_model, newdata = test_data, type = "prob")[, 2]

# Fairness evaluation
# We will use sex as the sensitive attribute and day_28_flg as the outcome.
# We choose threshold = 0.41 so that the overall FPR is around 5%.

# Evaluate Conditional Statistical Parity

eval_cond_stats_parity(
  data = test_data,
  outcome = "day_28_flg",
  group = "gender",
  group2 = "service_unit",
  condition = "MICU",
  probs = "pred",
  cutoff = 0.41
)


[Package fairmetrics version 1.0.4 Index]