VIMP_cfg
is a configuration class for estimating a variable importance measure
across all moderators. This provides a meaningful measure of which moderators
explain the most of the CATE surface.
Williamson, B. D., Gilbert, P. B., Carone, M., & Simon, N. (2021). Nonparametric variable importance assessment using machine learning techniques. Biometrics, 77(1), 9-22.
Williamson, B. D., Gilbert, P. B., Simon, N. R., & Carone, M. (2021). A general framework for inference on algorithm-agnostic variable importance. Journal of the American Statistical Association, 1-14.
estimand
String indicating the estimand to target.
sample_splitting
Logical indicating whether to use sample splitting in the calculation of variable importance.
linear
Logical indicating whether the variable importance assuming a linear model should be estimated.
new()
Create a new VIMP_cfg
object with specified model configuration.
VIMP_cfg$new(sample_splitting = TRUE, linear_only = FALSE)
sample_splitting
Logical indicating whether to use sample splitting in the calculation of variable importance. Choosing not to use sample splitting means that inference will only be valid for moderators with non-null importance.
linear_only
Logical indicating whether the variable importance should use only a single linear-only model. Variable importance measure will only be consistent for the population quantity if the true model of pseudo-outcomes is linear.
VIMP_cfg$new()
VIMP_cfg$new()
#> <VIMP_cfg>
#> Public:
#> clone: function (deep = FALSE)
#> estimand: VIMP
#> initialize: function (sample_splitting = TRUE, linear_only = FALSE)
#> linear: FALSE
#> sample_splitting: TRUE
## ------------------------------------------------
## Method `VIMP_cfg$new`
## ------------------------------------------------
VIMP_cfg$new()
#> <VIMP_cfg>
#> Public:
#> clone: function (deep = FALSE)
#> estimand: VIMP
#> initialize: function (sample_splitting = TRUE, linear_only = FALSE)
#> linear: FALSE
#> sample_splitting: TRUE