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.
estimandString indicating the estimand to target.
sample_splittingLogical indicating whether to use sample splitting in the calculation of variable importance.
linearLogical 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$new(sample_splitting = TRUE, linear_only = FALSE)sample_splittingLogical indicating whether to use sample splitting in the calculation of variable importance. Choosing not to use sample splitting means that inference will only be valid for moderators with non-null importance.
linear_onlyLogical indicating whether the variable importance should use only a single linear-only model. Variable importance measure will only be consistent for the population quantity if the true model of pseudo-outcomes is linear.
VIMP_cfg$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