The goal of faircfint is to facilitate estimation and inference for intersectional, counterfactual unfairness metrics. The metrics and techniques are described in “An intersectional framework for counterfactual fairness in risk prediction” and “Counterfactual fairness for small subgroups”.
You can install the development version of faircfint from GitHub with:
# install.packages("devtools")
devtools::install_github("swastvedt/faircfint")The main function, analysis_estimation, calculates the intersectional, counterfactual unfairness metrics from data (which must include risk predictions).
The ‘standard’ option for estimator_type uses the methods from “An intersectional framework for counterfactual fairness in risk prediction”. Options ‘small_internal’ and ‘small_borrow’ use the methods from “Counterfactual fairness for small subgroups”.
library(faircfint)
# Build propensity score model
pi_model_ex <- glm(as.factor(as.character(D)) ~ A1*A2 + A1 + A2 + S_prob + X.1 + X.2 + X.3 + X.4,
data = faircfint::ex_data_estimation, family = "binomial")
# Estimate counterfactual error rates and unfairness metrics, including bootstrap and null distribution
results <- analysis_estimation(data = faircfint::ex_data_estimation, cutoff = 0.5, estimator_type = "standard",
pi_model = pi_model_ex, pi_model_type = "glm", pi_xvars = c("X.1", "X.2", "X.3", "X.4"),
gen_null = T, bootstrap = 'rescaled')
# Estimate metrics using small subgroup option with no data borrowing
pi_model_ex_small <- glm(as.factor(as.character(D)) ~ A1*A2 + A1 + A2 + S_prob + X.1 + X.2 + X.3 + X.4,
data = ex_data_small, family = "binomial")
results_smallsub <- analysis_estimation(ex_data_small, cutoff = 0.5, estimator_type = "small_internal",
gen_null = T, R_null = 100, bootstrap = 'rescaled', B=100,
pi_model = pi_model_ex_small, pi_model_type = "glm", pi_xvars = c("X.1", "X.2", "X.3", "X.4"),
outcome_model_type = "glm", outcome_xvars = c("X_outcome.1", "X_outcome.2", "X_outcome.3", "X_outcome.4", "X_outcome.5", "X_outcome.6", "X_outcome.7", "X_outcome.8"),
fit_method_int = "multinomial", nfolds = 5, pa_xvars_int = c("X_pa.1", "X_pa.2", "X_pa.3", "X_pa.4", "X_pa.5", "X_pa.6", "X_pa.7"))
# Estimate metrics using small subgroup option with data borrowing
## Optional fitting of external data model prior to running main function
pa_model_ext_ex <- get_pa_ext_small(ex_data_external, fit_method_ext = "multinomial", pa_xvars_ext = c("X_pa.1", "X_pa.2", "X_pa.3", "X_pa.4", "X_pa.5"))
#> # weights: 42 (30 variable)
#> initial value 17917.594692
#> iter 10 value 17706.446512
#> iter 20 value 17650.998495
#> iter 30 value 17650.359482
#> final value 17650.355385
#> converged
results_borrow <- analysis_estimation(ex_data_small, cutoff = 0.5, estimator_type = "small_borrow",
gen_null = F, R_null = 100, bootstrap = 'none', B=100,
pi_model = pi_model_ex_small, pi_model_type = "glm", pi_xvars = c("X.1", "X.2", "X.3", "X.4"),
outcome_model_type = "glm", outcome_xvars = c("X_outcome.1", "X_outcome.2", "X_outcome.3", "X_outcome.4", "X_outcome.5", "X_outcome.6", "X_outcome.7", "X_outcome.8"),
fit_method_int = "multinomial", nfolds = 5, pa_xvars_int = c("X_pa.1", "X_pa.2", "X_pa.3", "X_pa.4", "X_pa.5", "X_pa.6", "X_pa.7"),
data_external = ex_data_external, fit_method_ext = "multinomial", pa_xvars_ext = c("X_pa.1", "X_pa.2", "X_pa.3", "X_pa.4", "X_pa.5"), borrow_metric = "brier",
pa_model_ext = pa_model_ext_ex)
# Plot results
results_plots <- get_plots(results, sampsize = 5000, alpha = 0.05, m_factor = 0.75)
# Example plot of negative (cFNR) versions of small subgroup metrics
#results_plots$metrics_pos