compute_stats_subgroup {aihuman} | R Documentation |
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 |
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
)