eval_treatment_equality: Examine Treatment Equality of a Model

In fairmetrics: Fairness Evaluation Metrics with Confidence Intervals for Binary Protected Attributes

Examine Treatment Equality of a Model

Description

This function evaluates Treatment Equality, a fairness criterion that assesses whether the ratio of false negatives to false positives is similar across groups defined by a binary protected attribute. Treatment Equality ensures that the model does not disproportionately favor or disadvantage any group in terms of the relative frequency of missed detections (false negatives) versus false alarms (false positives).

Usage

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

Arguments

data	Data frame containing the outcome, predicted outcome, and binary protected attribute
outcome	Name of the outcome variable
group	group Name of the binary protected attribute. Must consist of only two groups.
probs	Predicted probabilities
cutoff	Cutoff value for the predicted probabilities
confint	Logical indicating whether to calculate confidence intervals
alpha	The 1 - significance level for the confidence interval, default is 0.05
bootstraps	Number of bootstraps to use for confidence intervals
digits	Number of digits to round the results to, default is 2
message	Logical; if TRUE (default), prints a textual summary of the fairness evaluation. Only works if confint is TRUE.

Value

A list containing the following elements:

• False Negative / False Positive ratio for Group 1

• False Negative / False Positive ratio for Group 2

• Difference in False Negative / False Positive ratio

• Ratio in False Negative / False Positive ratio If confidence intervals are computed (confint = TRUE):

• A vector of length 2 containing the lower and upper bounds of the 95% confidence interval for the difference in False Negative / False Positive ratio

• A vector of length 2 containing the lower and upper bounds of the 95% confidence interval for the ratio in False Negative / False Positive ratio

See Also

eval_acc_parity, eval_bs_parity, eval_pos_pred_parity, eval_neg_pred_parity

Examples

library(fairmetrics)
library(dplyr)
library(magrittr)
library(randomForest)
# Data for tests
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 protected attribute and day_28_flg as the outcome.

# Evaluate Treatment Equality
eval_treatment_equality(
  data = test_data,
  outcome = "day_28_flg",
  group = "gender",
  probs = "pred",
  cutoff = 0.41,
  confint = TRUE,
  alpha = 0.05,
  bootstraps = 2500,
  digits = 2,
  message = FALSE
)

fairmetrics documentation built on Sept. 11, 2025, 9:09 a.m.