aihuman (version 1.0.0)

compute_bounds_aipw: Compute Risk (AI v. Human)

Description

Compute the difference in risk between AI and human decision makers using AIPW estimators.

Usage

compute_bounds_aipw(
  Y,
  A,
  D,
  Z,
  X = NULL,
  nuis_funcs,
  nuis_funcs_ai,
  true.pscore = NULL,
  l01 = 1
)

Value

A tibble the following columns:

• Z_focal: The focal treatment indicator. `1` indicates the treatment group.

• Z_compare: The comparison treatment indicator. `0` indicates the control group.

• X: Pretreatment covariate (if provided).

• fn_diff_lb: The lower bound of difference in false negatives

• fn_diff_ub: The upper bound of difference in false negatives

• fp_diff_lb: The lower bound of difference in false positives

• fp_diff_ub: The upper bound of difference in false positives

• loss_diff_lb: The lower bound of difference in loss

• loss_diff_ub: The upper bound of difference in loss

• fn_diff_lb_se: The standard error of the difference in false negatives

• fn_diff_ub_se: The standard error of the difference in false negatives

• fp_diff_lb_se: The standard error of the difference in false positives

• fp_diff_ub_se: The standard error of the difference in false positives

• loss_diff_lb_se: The standard error of the difference in loss

• loss_diff_ub_se: The standard error of the difference in loss

Arguments

Y

An observed outcome (binary: numeric vector of 0 or 1).

A

An observed AI recommendation (binary: numeric vector of 0 or 1).

D

An observed decision (binary: numeric vector of 0 or 1).

Z

A treatment indicator (binary: numeric vector of 0 or 1).

X

Pretreatment covariate used for subgroup analysis (vector). Must be the same length as Y, D, Z, and A if provided. Default is NULL.

nuis_funcs

output from compute_nuisance_functions

nuis_funcs_ai

output from compute_nuisance_functions_ai

true.pscore

A vector of true propensity scores (numeric), if available. Optional.

l01

Ratio of the loss between false positives and false negatives

Examples

compute_bounds_aipw(
  Y = NCAdata$Y,
  A = PSAdata$DMF,
  D = ifelse(NCAdata$D == 0, 0, 1),
  Z = NCAdata$Z,
  nuis_funcs = nuis_func,
  nuis_funcs_ai = nuis_func_ai,
  true.pscore = rep(0.5, nrow(NCAdata)),
  X = NULL,
  l01 = 1
)