slasso: S-learner, implemented via glmnet (lasso)

In xnie/rlearner: R-learner for Heterogeneous Treatment Effect Estimation

Description

S-learner, as proposed by Imai and Ratkovic (2013), implemented via glmnet (lasso)

Usage

slasso(
  x,
  w,
  y,
  alpha = 1,
  k_folds = NULL,
  foldid = NULL,
  lambda = NULL,
  lambda_choice = c("lambda.min", "lambda.1se"),
  penalty_factor = NULL
)

Arguments

x	the input features
w	the treatment variable (0 or 1)
y	the observed response (real valued)
alpha	tuning parameter for the elastic net
k_folds	number of folds for cross validation
foldid	user-supplied foldid. Must have length equal to length(w). If provided, it overrides the k_folds option.
lambda	user-supplied lambda sequence for cross validation
lambda_choice	how to cross-validate; choose from "lambda.min" or "lambda.1se"
penalty_factor	user-supplied penalty factor, must be of length the same as number of features in x

Examples

## Not run: 
n = 100; p = 10

x = matrix(rnorm(n*p), n, p)
w = rbinom(n, 1, 0.5)
y = pmax(x[,1], 0) * w + x[,2] + pmin(x[,3], 0) + rnorm(n)

slasso_fit = slasso(x, w, y)
slasso_est = predict(slasso_fit, x)

## End(Not run)

xnie/rlearner documentation built on April 11, 2021, 12:49 a.m.