AIPW for the mean (and linear projections of the EIF) with missing observations
aipw(response_model, propensity_model, formula = ~1, data, ...)Model for the response given covariates (ml_model or formula)
Optional missing data mechanism model (propensity model) (ml_model or formula)
design specifying the OLS estimator with outcome given by the EIF
data.frame
additional arguments (see 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)] 0.05148 0.08338 -0.1119 0.2149 0.537
#> ───────────
#> (Intercept) 0.05148 0.08380 -0.1128 0.2157 0.539