AIPW for the mean (and linear projections of the EIF) with missing observations
aipw(response_model, data, formula = ~1, missing_model, ...)Model for the response given covariates (ml_model or formula)
data.frame
design specifying the OLS estimator with outcome given by the EIF
Optional missing_model (ml_model or formula). By default will use the same design as the response_model.
arguments to cate
m <- lvm(y ~ x+z, r ~ x)
distribution(m,~ r) <- binomial.lvm()
transform(m, y0~r+y) <- function(x) { x[x[,1]==0,2] <- NA; x[,2] }
d <- sim(m,1e3,seed=1)
aipw(y0 ~ x, data=d)
#> Estimate Std.Err 2.5% 97.5% P-value
#> E[y0(1)] 0.05351 0.08466 -0.1124 0.2194 0.5274
#> ───────────
#> (Intercept) 0.05351 0.08466 -0.1124 0.2194 0.5274