Plot histogram or density of Sample Average Treatment Effects

Plot a histogram or density of the Sample Average Treatment Effect (SATE). The Sample Average Treatment Effect is derived from taking the difference of each individual's observed outcome and a predicted counterfactual outcome from a BART model averaged over the population. The mean of SATE will resemble means of CATE and PATE but will account for the least uncertainty.

Usage

plot_SATE(
  .model,
  type = c("histogram", "density"),
  ci_80 = FALSE,
  ci_95 = FALSE,
  reference = NULL,
  .mean = FALSE,
  .median = FALSE,
  check_overlap = FALSE,
  overlap_rule = c("none", "sd", "chisq")
)

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 x-axis value
.mean: TRUE/FALSE. Show the mean reference line
.median: TRUE/FALSE. Show the median reference line
check_overlap: TRUE/FALSE. Check if any overlap rules are applicable
overlap_rule: enter overlap rules to view how different bartCause removal rules would have influenced results. Only applicable if check_overlap is TRUE.

Value

ggplot object

Author

George Perrett, Joseph Marlo

Examples

# \donttest{
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'
)
#> fitting treatment model via method 'bart'
#> fitting response model via method 'bart'
plot_SATE(model_results)
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

# }