Conditional average treatment effect estimation via Double Machine Learning

cate_link(
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

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'

...

## Value

cate.targeted object

## Author

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)
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)
}