compute_stats_aipw: Compute Risk (Human+AI v. Human)

In aihuman: Experimental Evaluation of Algorithm-Assisted Human Decision-Making

Compute Risk (Human+AI v. Human)

Description

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

Usage

compute_stats_aipw(Y, D, Z, nuis_funcs, true.pscore = NULL, X = NULL, l01 = 1)

Arguments

Y	An observed outcome (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).
nuis_funcs	output from compute_nuisance_functions
true.pscore	A vector of true propensity scores (numeric), if available. Optional.
X	Pretreatment covariate used for subgroup analysis (vector). Must be the same length as Y, D, Z, and A if provided. Default is NULL.
l01	Ratio of the loss between false positives and false negatives

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).

• loss_diff: The difference in loss between human+AI and human decision

• loss_diff_se: The standard error of the difference in loss

• fn_diff: The difference in false negatives between human+AI and human decision

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

• fp_diff: The difference in false positives between human+AI and human decision

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

Examples

compute_stats_aipw(
  Y = NCAdata$Y,
  D = ifelse(NCAdata$D == 0, 0, 1),
  Z = NCAdata$Z,
  nuis_funcs = nuis_func,
  true.pscore = rep(0.5, nrow(NCAdata)),
  X = NULL,
  l01 = 1
)

aihuman documentation built on April 12, 2025, 1:47 a.m.