Conditional Average Treatment Effect estimation

Conditional Average Treatment Effect estimation via Double Machine Learning

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

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
#>             Estimate Std.Err   2.5%  97.5%   P-value
#> (Intercept)   0.6290  0.1592 0.3170 0.9411 7.779e-05
#> z1            0.4525  0.1778 0.1041 0.8010 1.091e-02
#> z2            0.5482  0.1574 0.2397 0.8567 4.956e-04
#> z3            0.7710  0.2121 0.3553 1.1867 2.774e-04