aihuman (version 1.0.0)

plot_diff_human_aipw: Visualize Difference in Risk (Human+AI v. Human)

Description

Visualize the difference in risk between human+AI and human decision makers using AIPW estimators. Generate a plot based on the overall agreement and subgroup-specific agreement.

Usage

plot_diff_human_aipw(
  Y,
  D,
  Z,
  V = NULL,
  l01 = 1,
  nuis_funcs = NULL,
  true.pscore = NULL,
  subgroup1,
  subgroup2,
  label.subgroup1 = "Subgroup 1",
  label.subgroup2 = "Subgroup 2",
  x.order = NULL,
  p.title = NULL,
  p.lb = -1,
  p.ub = 1,
  y.lab = "Impact of PSA",
  p.label = c("PSA harms", "PSA helps")
)

Value

A ggplot object.

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).

V

A matrix of pretreatment covariates (numeric matrix). Optional.

l01

Ratio of the loss between false positives and false negatives. Default 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.

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".

x.order

An order for the x-axis (character vector). Default NULL.

p.title

A title for the plot (character). Default NULL.

p.lb

A lower bound for the y-axis (numeric). Default -1.

p.ub

An upper bound for the y-axis (numeric). Default 1.

y.lab

A label for the y-axis (character). Default "Impact of PSA".

p.label

A vector of two labels for the annotations (character). Default c("PSA harms", "PSA helps").

Examples

plot_diff_human_aipw(
  Y = NCAdata$Y,
  D = ifelse(NCAdata$D == 0, 0, 1),
  Z = NCAdata$Z,
  nuis_funcs = nuis_func,
  true.pscore = rep(0.5, nrow(NCAdata)),
  l01 = 1,
  subgroup1 = ifelse(NCAdata$White == 1, "White", "Non-white"),
  subgroup2 = ifelse(NCAdata$Sex == 1, "Male", "Female"),
  label.subgroup1 = "Race",
  label.subgroup2 = "Gender",
  x.order = c("Overall", "Non-white", "White", "Female", "Male"),
  p.title = NULL, p.lb = -0.3, p.ub = 0.3
)