cate_link: Conditional Relative Risk estimation

In targeted: Targeted Inference

Conditional Relative Risk estimation

Description

Conditional average treatment effect estimation via Double Machine Learning

Usage

cate_link(
  treatment,
  link = "identity",
  response_model,
  propensity_model,
  importance_model,
  contrast = c(1, 0),
  data,
  nfolds = 5,
  type = "dml1",
  ...
)

Arguments

treatment	formula specifying treatment and variables to condition on
link	Link function
response_model	SL object
propensity_model	SL object
importance_model	SL object
contrast	treatment contrast (default 1 vs 0)
data	data.frame
nfolds	Number of folds
type	'dml1' or 'dml2'
...	additional arguments to SuperLearner

Value

cate.targeted object

Author(s)

Klaus Kähler Holst & Andreas Nordland

Examples

# Example 1:
sim1 <- function(n=1e4,
                 seed=NULL,
                 return_model=FALSE, ...){
suppressPackageStartupMessages(require("lava"))
if (!is.null(seed)) set.seed(seed)
m <- lava::lvm()
distribution(m, ~x) <- gaussian.lvm()
distribution(m, ~v) <- gaussian.lvm(mean = 10)
distribution(m, ~a) <- binomial.lvm("logit")
regression(m, "a") <- function(v, x){.1*v + x}
distribution(m, "y") <- gaussian.lvm()
regression(m, "y") <- function(a, v, x){v+x+a*x+a*v*v}
if (return_model) return(m)
lava::sim(m, n = n)
}

if (require("SuperLearner",quietly=TRUE)) {
  d <- sim1(n = 1e3, seed = 1)
  e <- cate_link(data=d,
           type = "dml2",
           treatment = a ~ v,
           response_model = y~ a*(x + v + I(v^2)),
           importance_model = SL(D_ ~ v + I(v^2)),
           nfolds = 10)
  summary(e) # the true parameters are c(1,1)
}

targeted documentation built on May 29, 2024, 2:08 a.m.