plot_CATE: Plot the histogram or density of the Conditional Average...

In joemarlo/plotBart: Diagnostic and Plotting Functions to Supplement 'bartCause'

Plot the histogram or density of the Conditional Average Treatment Effect

Description

Plot the conditional average treatment effect (CATE) of a 'bartCause' model. The conditional average treatment effect is derived from taking the difference between predictions for each individual under the control condition and under the treatment condition averaged over the population. Means of the CATE distribution will resemble SATE and PATE but the CATE distribution accounts for more uncertainty than SATE and less uncertainty than PATE.

Usage

plot_CATE(
  .model,
  type = c("histogram", "density"),
  ci_80 = FALSE,
  ci_95 = FALSE,
  reference = NULL,
  .mean = FALSE,
  .median = FALSE
)

Arguments

.model	a model produced by 'bartCause::bartc()'
type	histogram or density
ci_80	TRUE/FALSE. Show the 80% credible interval?
ci_95	TRUE/FALSE. Show the 95% credible interval?
reference	numeric. Show a vertical reference line at this value
.mean	TRUE/FALSE. Show the mean reference line
.median	TRUE/FALSE. Show the median reference line

Value

ggplot object

Author(s)

George Perrett, Joseph Marlo

Examples

data(lalonde)
confounders <- c('age', 'educ', 'black', 'hisp', 'married', 'nodegr')
model_results <- bartCause::bartc(
 response = lalonde[['re78']],
 treatment = lalonde[['treat']],
 confounders = as.matrix(lalonde[, confounders]),
 estimand = 'ate',
 commonSup.rule = 'none'
)
plot_CATE(model_results)

joemarlo/plotBart documentation built on May 31, 2024, 12:22 p.m.