table_agreement {aihuman} | R Documentation |
Table of Agreement
Description
Estimate the impact of AI recommendations on the agreement between human decisions and AI recommendations using a difference-in-means estimator of an indicator 1\{D_i = A_i\}
.
Generate a table based on the overall agreement and subgroup-specific agreement.
Usage
table_agreement(
Y,
D,
Z,
A,
subgroup1,
subgroup2,
label.subgroup1 = "Subgroup 1",
label.subgroup2 = "Subgroup 2"
)
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). |
subgroup1 |
A pretreatment covariate used for subgroup analysis (vector). |
subgroup2 |
A pretreatment covariate used for subgroup analysis (vector). |
label.subgroup1 |
A label for subgroup1 (character). Default "Subgroup 1". |
label.subgroup2 |
A label for subgroup2 (character). Default "Subgroup 2". |
Value
A tibble with the following columns:
-
cov
: Subgroup label. -
X
: Subgroup value. -
agree_diff
: Difference in agreement between human decisions and AI recommendations. -
agree_diff_se
: Standard error of the difference in agreement. -
ci_lb
: Lower bound of the 95% confidence interval. -
ci_ub
: Upper bound of the 95% confidence interval.
Examples
table_agreement(
Y = NCAdata$Y,
D = ifelse(NCAdata$D == 0, 0, 1),
Z = NCAdata$Z,
A = PSAdata$DMF,
subgroup1 = ifelse(NCAdata$White == 1, "White", "Non-white"),
subgroup2 = ifelse(NCAdata$Sex == 1, "Male", "Female"),
label.subgroup1 = "Race",
label.subgroup2 = "Gender"
)