cate: Conditional Average Treatment Effect estimation

In targeted: Targeted Inference

Conditional Average Treatment Effect estimation

Description

Conditional Average Treatment Effect estimation with cross-fitting.

Usage

cate(
  response.model,
  propensity.model,
  cate.model = ~1,
  calibration.model = NULL,
  data,
  contrast,
  nfolds = 1,
  rep = 1,
  silent = FALSE,
  stratify = FALSE,
  mc.cores = NULL,
  rep.type = c("nuisance", "average"),
  var.type = "IC",
  second.order = TRUE,
  response_model = deprecated,
  cate_model = deprecated,
  propensity_model = deprecated,
  treatment = deprecated,
  ...
)

Arguments

response.model	formula or learner object (formula => learner_glm)
propensity.model	formula or learner object (formula => learner_glm)
cate.model	formula specifying regression design for conditional average treatment effects
calibration.model	linear calibration model. Specify covariates in addition to predicted potential outcomes to include in the calibration.
data	data.frame
contrast	treatment contrast (default 1 vs 0)
nfolds	number of folds
rep	number of replications of cross-fitting procedure
silent	suppress all messages and progressbars
stratify	if TRUE the response.model will be stratified by treatment
mc.cores	(optional) number of cores. parallel::mcmapply used instead of future
rep.type	repeated cross-fitting applied by averaging nuisance models (rep.type="nuisance") or by average estimates from each replication (rep.type="average").
var.type	when equal to "IC" the asymptotic variance is derived from the influence function. Otherwise, based on expressions in Bannick et al. (2025) valid under different covariate-adaptive randomization schemes (only available for ATE and when calibration.model is also specified)
second.order	add seconder order term to IF to handle misspecification of outcome models
response_model	Deprecated. Use response.model instead.
cate_model	Deprecated. Use cate.model instead.
propensity_model	Deprecated. Use propensity.model instead.
treatment	Deprecated. Use cate.model instead.
...	additional arguments to future.apply::future_mapply

Details

We have observed data (Y,A,W) where Y is the response variable, A the binary treatment, and W covariates. We further let V be a subset of the covariates. Define the conditional potential mean outcome

\psi_{a}(P)(V) = E_{P}[E_{P}(Y\mid A=a, W)|V]

and let m(V; \beta) denote a parametric working model, then the target parameter is the mean-squared error

\beta(P) = \operatorname{argmin}_{\beta} E_{P}[\{\Psi_{1}(P)(V)-\Psi_{0}(P)(V)\} - m(V; \beta)]^{2}

Value

cate.targeted object

Author(s)

Klaus Kähler Holst, Andreas Nordland

References

Mark J. van der Laan (2006) Statistical Inference for Variable Importance, The International Journal of Biostatistics.

Examples

sim1 <- function(n=1000, ...) {
  w1 <- rnorm(n)
  w2 <- rnorm(n)
  a <- rbinom(n, 1, plogis(-1 + w1))
  y <- cos(w1) + w2*a + 0.2*w2^2 + a + rnorm(n)
  data.frame(y, a, w1, w2)
}

d <- sim1(5000)
## ATE
cate(cate.model=~1,
     response.model=y~a*(w1+w2),
     propensity.model=a~w1+w2,
     data=d)
## CATE
cate(cate.model=~1+w2,
     response.model=y~a*(w1+w2),
     propensity.model=a~w1+w2,
     data=d)

## Not run:  ## superlearner example
mod1 <- list(
   glm = learner_glm(y~w1+w2),
   gam = learner_gam(y~s(w1) + s(w2))
)
s1 <- learner_sl(mod1, nfolds=5)
cate(cate.model=~1,
     response.model=s1,
     propensity.model=learner_glm(a~w1+w2, family=binomial),
     data=d,
     stratify=TRUE)

## End(Not run)

targeted documentation built on Jan. 12, 2026, 9:08 a.m.