plot_moderator_c_pd: Partial dependency plot of a continuous moderating variable

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

Partial dependency plot of a continuous moderating variable

Description

Plot a partial dependency plot with a continuous covariate from a 'bartCause' model. Identify treatment effect variation predicted across levels of a continuous variable.

Usage

plot_moderator_c_pd(.model, moderator, n_bins = NULL)

Arguments

.model	a model produced by 'bartCause::bartc()'
moderator	the moderator as a vector
n_bins	number of bins to cut the moderator with. Defaults to the lesser of 15 and number of distinct levels of the moderator

Details

Partial dependency plots are one way to evaluate heterogeneous treatment effects that vary by values of a continuous covariate. For more information on partial dependency plots from BART causal inference models see Green and Kern 2012.

Value

ggplot object

Author(s)

George Perrett, Joseph Marlo

References

Green, D. P., & Kern, H. L. (2012). Modeling heterogeneous treatment effects in survey experiments with Bayesian additive regression trees. Public opinion quarterly, 76(3), 491-511.

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',
 commonSuprule = 'none',
 keepTrees = TRUE
)
plot_moderator_c_pd(model_results, lalonde$age)

plotBart documentation built on May 27, 2022, 9:12 a.m.