AIPW estimator — aipw • targeted

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

Usage

aipw(response.model, propensity.model, formula = ~1, data, ...)

Arguments

response.model: (learner or formula) Model for the response given covariates
propensity.model: (learner or formula) Missing data mechanism model and if omitted a logistic regression model with the same covariates as response.model is used
formula: design specifying the OLS estimator with outcome given by the EIF (see cate)
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,5e3,seed=1)

aipw(y0 ~ x, ~ x + z, data=d)
#>             Estimate Std.Err     2.5%   97.5% P-value
#> (Intercept) -0.02208 0.03092 -0.08269 0.03852  0.4751