cate: Conditional Average Treatment Effect estimation

In targeted: Targeted Inference

Conditional Average Treatment Effect estimation

Description

Conditional Average Treatment Effect estimation via Double Machine Learning

Usage

cate(
  treatment,
  response_model,
  propensity_model,
  contrast = c(1, 0),
  data,
  nfolds = 5,
  type = "dml2",
  silent = FALSE,
  stratify = FALSE,
  mc.cores,
  ...
)

Arguments

treatment	formula specifying treatment and variables to condition on
response_model	formula or ml_model object (formula => glm)
propensity_model	formula or ml_model object (formula => glm)
contrast	treatment contrast (default 1 vs 0)
data	data.frame
nfolds	Number of folds
type	'dml1' or 'dml2'
silent	supress all messages and progressbars
stratify	If TRUE the response_model will be stratified by treatment
mc.cores	mc.cores Optional number of cores. parallel::mcmapply used instead of future
...	additional arguments to future.apply::future_mapply

Value

cate.targeted object

Author(s)

Klaus Kähler Holst

Examples

sim1 <- function(n=1e4,
                 seed=NULL,
                 return_model=FALSE, ...) {
suppressPackageStartupMessages(require("lava"))
if (!is.null(seed)) set.seed(seed)
m <- lava::lvm()
regression(m, ~a) <- function(z1,z2,z3,z4,z5)
         cos(z1)+sin(z1*z2)+z3+z4+z5^2
regression(m, ~u) <- function(a,z1,z2,z3,z4,z5)
        (z1+z2+z3)*a + z1+z2+z3 + a
distribution(m, ~a) <- binomial.lvm()
if (return_model) return(m)
lava::sim(m, n, p=par)
}

d <- sim1(200)
e <- cate(a ~ z1+z2+z3, response=u~., data=d)
e

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