mn.aipw: Augmented inverse probability weighted estimation of...

In RCAL: Regularized Calibrated Estimation

Description

This function implements augmented inverse probability weighted (IPW) estimation of population means with missing data, provided both fitted propensity scores and fitted values from outcome regression.

Usage

mn.aipw(y, tr, fp, fo, off = 0)

Arguments

y	An n x 1 vector of outcomes with missing data.
tr	An n x 1 vector of non-missing indicators (=1 if y is observed or 0 if y is missing).
fp	An n x 1 vector of fitted propensity scores.
fo	An n x 1 vector of fitted values from outcome regression.
off	An offset value (e.g., the true value in simulations) used to calculate the z-statistic.

Value

one	The direct IPW estimate of 1.
ipw	The ratio IPW estimate.
or	The outcome regression estimate.
est	The augmented IPW estimate.
var	The estimated variance associated with the augmented IPW estimate.
ze	The z-statistic for the augmented IPW estimate, compared to off.

References

Tan, Z. (2020a) Regularized calibrated estimation of propensity scores with model misspecification and high-dimensional data, Biometrika, 107, 137<e2><80><93>158.

Tan, Z. (2020b) Model-assisted inference for treatment effects using regularized calibrated estimation with high-dimensional data, Annals of Statistics, 48, 811<e2><80><93>837.

RCAL documentation built on Nov. 8, 2020, 4:22 p.m.