The goal of estimateit
is to estimate marginal effects using inverse
probability weights generated by
weightit
. Effects are
generated using svyglm
and svycontrast
from thesurvey package.
They include the effect for the treated, the effect for the untreated or
control, their difference, their relative difference, and their odds
ratio. As the previous sentence implies estimateit
only works with
binary exposures and binary outcomes at present.
You can install the development version of estimateit
from
GitHub with:
# install.packages("devtools")
devtools::install_github("frankpopham/estimateit")
## A simple example
library(WeightIt)
library(tibble)
library(tidyr)
dfvi <- tibble(
C = rep(0:1, each = 4),
X = rep(0:1, times = 4),
Y = rep(0:1, times = 2, each = 2),
N = c(96, 36, 64, 54, 120, 120, 30, 480)
) %>%
uncount(N)
W1 <- weightit(X ~ C, data = dfvi,
method = "ps", estimand = "ATE")
summary(W1)
E1 <- estimateit(weightitobj=W1, outcome=Y, data=dfvi)
E1
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