AIPW estimator

Source: R/aipw.R

AIPW for the mean (and linear projections of the EIF) with missing observations

aipw(response_model, propensity_model, formula = ~1, data, ...)

Arguments

response_model

Model for the response given covariates (learner or formula)

propensity_model

Optional missing data mechanism model (propensity model) (learner or formula)

formula

design specifying the OLS estimator with outcome given by the EIF

data

data.frame

...

additional arguments (see cate())

Examples

m <- lava::lvm(y ~ x+z, r ~ x) |>
     lava::distribution(~ r, value = lava::binomial.lvm()) |>
     transform(y0~r+y, value = \(x) { x[x[,1]==0,2] <- NA; x[,2] })
d <- lava::sim(m,1e3,seed=1)

aipw(y0 ~ x, data=d)
#>             Estimate Std.Err    2.5%  97.5% P-value
#> (Intercept)  0.05148 0.08338 -0.1119 0.2149   0.537