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Examine Treatment Equality of a Model

eval_treatment_equality.Rd

This function evaluates Treatment Equality, a fairness criterion that assesses whether the ratio of false negatives to false positives is similar across groups (e.g., based on gender or race). 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 sensitive attribute

outcome

Name of the outcome variable

group

Name of the sensitive attribute

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

Whether to print the results, default 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_pred_parity

Examples

# \donttest{
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 sensitive 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
)
#>        Metric GroupFemale GroupMale Difference   95% Diff CI Ratio 95% Ratio CI
#> 1 FN/FP Ratio        1.03      3.24      -2.21 [-4.44, 0.02]  0.32  [0.14, 0.7]
# }