Description Usage Arguments Value References
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.
1 | mn.aipw(y, tr, fp, fo, off = 0)
|
y |
An n x 1 vector of outcomes with missing data. |
tr |
An n x 1 vector of non-missing indicators (=1 if |
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. |
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 |
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.
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