hte_plot: hte_plot

In DeepLearningCausal: Causal Inference with Super Learner and Deep Neural Networks

hte_plot

Description

Produces plot to illustrate sub-group Heterogeneous Treatment Effects (HTE) of estimated CATEs from metalearner_ensemble and metalearner_neural, as well as PATT-C from pattc_ensemble and pattc_neural.

Usage

hte_plot(
  x,
  ...,
  boot = TRUE,
  n_boot = 1000,
  cut_points = NULL,
  custom_labels = NULL,
  zero_int = TRUE,
  selected_vars = NULL
)

Arguments

x	estimated model from metalearner_ensemble, metalearner_neural, pattc_ensemble, or pattc_neural.
...	Additional arguments
boot	logical for using bootstraps to estimate confidence intervals.
n_boot	number of bootstrap iterations. Only used with boot = TRUE.
cut_points	numeric vector for cut-off points to generate subgroups from covariates. If left blank a vector generated from median values will be used.
custom_labels	character vector for the names of subgroups.
zero_int	logical for vertical line at 0 x intercept.
selected_vars	vector for names of covariates to use for subgroups.

Value

ggplot object illustrating subgroup HTE and 95% confidence intervals.

Examples

# load dataset
set.seed(123456)
xlearner_nn <- metalearner_neural(cov.formula = support_war ~ age +
                                  income  + employed  + job_loss,
                                  data = exp_data,
                                  treat.var = "strong_leader",
                                  meta.learner.type = "X.Learner",
                                  stepmax = 2e+9,
                                  nfolds = 5,
                                  algorithm = "rprop+",
                                  hidden.layer = c(3),
                                  linear.output = FALSE,
                                  binary.preds = FALSE)

hte_plot(xlearner_nn)
hte_plot(xlearner_nn,
        selected_vars = c("age", "income"),
        cut_points = c(33, 3),
        custom_labels = c("Age <= 33", "Age > 33", "Income <= 3", "Income > 3"),
        n_boot = 500)
                                  

DeepLearningCausal documentation built on Nov. 6, 2025, 5:08 p.m.