compute_stats {aihuman} | R Documentation |
Compute Risk (Human+AI v. Human)
Description
Compute the difference in risk between human+AI and human decision makers using difference-in-means estimators.
Usage
compute_stats(Y, D, Z, 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). |
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(
Y = NCAdata$Y,
D = ifelse(NCAdata$D == 0, 0, 1),
Z = NCAdata$Z,
X = NULL,
l01 = 1
)