Estimation of the Average Treatment Effect among Responders
Usage
RATE(
response,
post.treatment,
treatment,
data,
family = gaussian(),
M = 5,
pr.treatment,
treatment.level,
SL.args.response = list(family = gaussian(), SL.library = c("SL.mean", "SL.glm")),
SL.args.post.treatment = list(family = binomial(), SL.library = c("SL.mean", "SL.glm")),
preprocess = NULL,
efficient = TRUE,
...
)Arguments
- response
Response formula (e.g, Y ~ D*A)
- post.treatment
Post treatment marker formula (e.g., D ~ W)
- treatment
Treatment formula (e.g, A ~ 1)
- data
data.frame
- family
Exponential family for response (default gaussian)
- M
Number of folds in cross-fitting (M=1 is no cross-fitting)
- pr.treatment
(optional) Randomization probability of treatment.
- treatment.level
Treatment level in binary treatment (default 1)
- SL.args.response
Arguments to SuperLearner for the response model
- SL.args.post.treatment
Arguments to SuperLearner for the post treatment indicator
- preprocess
(optional) Data preprocessing function
- efficient
If TRUE, the estimate will be efficient. If FALSE, the estimate will be a simple plug-in estimate.
- ...
Additional arguments to lower level functions