Responder Average Treatment Effect

Estimation of the Average Treatment Effect among Responders

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

Value

estimate object

Author

Andreas Nordland, Klaus K. Holst