compute_stats_subgroup: Compute Risk (Human+AI v. Human) for a Subgroup Defined by AI...

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

Compute Risk (Human+AI v. Human) for a Subgroup Defined by AI Recommendation

Description

Compute the difference in risk between human+AI and human decision makers, for a subgroup \{A_i = a\}, using AIPW estimators. This can be used for computing how the decision maker overrides the AI recommendation.

Usage

compute_stats_subgroup(
  Y,
  D,
  Z,
  A,
  a = 1,
  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).
A	An AI recommendation (binary: numeric vector of 0 or 1).
a	A specific AI recommendation value to create the subset (numeric: 0 or 1).
nuis_funcs	output from compute_nuisance_functions. If NULL, the function will compute the nuisance functions using the provided data. Note that V must be provided if nuis_funcs is NULL.
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

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

• tn_fn_diff_se: The standard error of the difference in true negatives and false negatives

• tp_diff: The difference in true positives between human+AI and human decision

• tp_diff_se: The standard error of the difference in true positives

• tn_diff: The difference in true negatives between human+AI and human decision

• tn_diff_se: The standard error of the difference in true negatives

• 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_subgroup(
  Y = NCAdata$Y,
  D = ifelse(NCAdata$D == 0, 0, 1),
  Z = NCAdata$Z,
  A = PSAdata$DMF,
  a = 1,
  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.