ate: AIPW estimator for Average Treatement Effect

In targeted: Targeted Inference

AIPW estimator for Average Treatement Effect

Description

Augmented Inverse Probability Weighting estimator for the Average (Causal) Treatment Effect.

Usage

ate(
  formula,
  data = parent.frame(),
  weights,
  binary = TRUE,
  nuisance = NULL,
  propensity = nuisance,
  all,
  missing = FALSE,
  labels = NULL,
  ...
)

Arguments

formula	Formula (see details below)
data	data.frame
weights	optional frequency weights
binary	Binary response (default TRUE)
nuisance	outcome regression formula
propensity	propensity model formula
all	If TRUE all standard errors are calculated (default TRUE when exposure only has two levels)
missing	If TRUE a missing data (AIPW) estimator is returned
labels	Optional treatment labels
...	Additional arguments to lower level functions

Details

The formula may either be specified as: response ~ treatment | nuisance-formula | propensity-formula

For example: ate(y~a | x+z+a | x*z, data=...)

Alternatively, as a list: ate(list(y~a, ~x+z, ~x*z), data=...)

Or using the nuisance (and propensity argument): ate(y~a, nuisance=~x+z, ...)

Value

An object of class 'ate.targeted' is returned. See targeted-class for more details about this class and its generic functions.

Author(s)

Klaus K. Holst

Examples

m <- lvm(y ~ a+x, a~x)
distribution(m,~ a+y) <- binomial.lvm()
d <- sim(m,1e3,seed=1)

a <- ate(y ~ a, nuisance=~x, data=d)
summary(a)

# Multiple treatments
m <- lvm(y ~ a+x, a~x)
distribution(m,~ y) <- binomial.lvm()
m <- ordinal(m, K=4, ~a)
transform(m, ~a) <- factor
d <- sim(m, 1e4, seed=1)
(a <- ate(y~a|a*x|x, data=d))

# Comparison with randomized experiment
m0 <- cancel(m, a~x)
d0 <- sim(m0,2e5)
lm(y~a-1,d0)

# Choosing a different contrast for the association measures
summary(a, contrast=c(2,4))

targeted documentation built on Oct. 26, 2022, 1:09 a.m.