vt2_lasso: Estimate the CATE using the Lasso for Step 2

In jackmwolf/tehtuner: Fit and Tune Models to Detect Treatment Effect Heterogeneity

Estimate the CATE using the Lasso for Step 2

Description

Estimate the CATE using the Lasso for Step 2

Usage

vt2_lasso(z, data, Trt, Y, theta)

Arguments

z	a numeric vector of estimated CATEs from Step 1
data	a data frame containing a response, binary treatment indicators, and covariates.
Trt	a string specifying the name of the column of data contains the treatment indicators.
Y	a string specifying the name of the column of data contains the response.
theta	lasso penalty parameter (lambda)

Value

a list of length 3 containing the following elements:

mod	an object of class glmnet. See glmnet.
coefficients	coefficients associated with the penalty parameter theta.
fitted.values	predicted values associated with the penalty parameter theta.

See Also

Other VT Step 2 functions: vt2_classtree(), vt2_ctree(), vt2_rtree()

jackmwolf/tehtuner documentation built on June 10, 2025, 10:32 p.m.