crr: Conditional Relative Risk estimation

In targeted: Targeted Inference

Conditional Relative Risk estimation

Description

Conditional Relative Risk estimation via Double Machine Learning

Usage

crr(
  treatment,
  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
response_model	SL object (outcome/response model)
propensity_model	SL object (treatment/propensity model)
importance_model	SL object (conditional expectation of outcome model on variables conditioned on in treatment argument)
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

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 = 5)
  distribution(m, ~a) <- binomial.lvm("logit")
  regression(m, "a") <- function(v, x){.2*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)
}

d <- sim1(n = 1e3, seed = 1)
if (require("SuperLearner",quietly=TRUE)) {
  e <- crr(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 = 2)
  summary(e) # the true parameters are c(1,1)
}

targeted documentation built on Oct. 26, 2022, 1:09 a.m.