VIMP_cfg {tidyhte}

Configuration of Variable Importance

Description

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.

Public fields

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.

Methods

Public methods

• VIMP_cfg$new()

• VIMP_cfg$clone()

Method new()

Create a new VIMP_cfg object with specified model configuration.

Usage

VIMP_cfg$new(sample_splitting = TRUE, linear_only = FALSE)

Arguments

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.

Returns

A new VIMP_cfg object.

Examples

VIMP_cfg$new()

Method clone()

The objects of this class are cloneable with this method.

Usage

VIMP_cfg$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

References

• 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.

Examples

VIMP_cfg$new()

## ------------------------------------------------
## Method `VIMP_cfg$new`
## ------------------------------------------------

VIMP_cfg$new()