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Examine Conditional Use Accuracy Equality of a Model

eval_cond_acc_equality.Rd

This function evaluates Conditional Use Accuracy Equality, a fairness criterion that requires predictive performance to be similar across groups when a model makes positive or negative predictions.

Usage

eval_cond_acc_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, it must be binary

group

Name of the sensitive attribute

probs

Name of the predicted outcome variable

cutoff

Threshold for the predicted outcome, default is 0.5

confint

Whether to compute 95% confidence interval, default is TRUE

alpha

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

bootstraps

Number of bootstrap samples, default is 2500

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:

  • PPV_Group1: Positive Predictive Value for the first group

  • PPV_Group2: Positive Predictive Value for the second group

  • PPV_Diff: Difference in Positive Predictive Value

  • NPV_Group1: Negative Predictive Value for the first group

  • NPV_Group2: Negative Predictive Value for the second group

  • NPV_Diff: Difference in Negative Predictive Value If confidence intervals are computed (confint = TRUE):

  • PPV_Diff_CI: A vector of length 2 containing the lower and upper bounds of the 95% confidence interval for the difference in Positive Predictive Value

  • NPV_Diff_CI: A vector of length 2 containing the lower and upper bounds of the 95% confidence interval for the difference in Negative Predictive Value

See also

eval_acc_parity

Examples

# \donttest{
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 Use Accuracy Equality
eval_cond_acc_equality(
  data = test_data,
  outcome = "day_28_flg",
  group = "gender",
  probs = "pred",
  cutoff = 0.41
)
#> There is not enough evidence that the model does not satisfy
#>             conditional use accuracy equality.
#>     Metric GroupFemale GroupMale  Difference                       95% CI
#> 1 PPV; NPV  0.62; 0.92 0.66; 0.9 -0.04; 0.02 [-0.24, 0.16]; [-0.02, 0.06]
# }