# Partial dependency plot of a continuous moderating variable

Source:`R/plot_moderators.R`

`plot_moderator_c_pd.Rd`

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

## 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.

## 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

```
# \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',
commonSuprule = 'none',
keepTrees = TRUE
)
#> fitting treatment model via method 'bart'
#> fitting response model via method 'bart'
plot_moderator_c_pd(model_results, lalonde$age)
# }
```