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

In RCAL: Regularized Calibrated Estimation

Description

This function implements augmented inverse probability weighted (IPW) estimation of average treatment effects (ATEs), provided both fitted propensity scores and fitted values from outcome regression.

Usage

ate.aipw(y, tr, mfp, mfo, off = NULL)

Arguments

y	An n x 1 vector of observed outcomes.
tr	An n x 1 vector of treatment indicators (=1 if treated or 0 if untreated).
mfp	An n x 2 matrix of fitted propensity scores for untreated (first column) and treated (second column).
mfo	An n x 2 matrix of fitted values from outcome regression, for untreated (first column) and treated (second column).
off	A 2 x 1 vector of offset values (e.g., the true values in simulations) used to calculate the z-statistics.

Value

one	A 2 x 1 vector of direct IPW estimates of 1.
ipw	A 2 x 1 vector of ratio IPW estimates of means.
or	A 2 x 1 vector of outcome regression estimates of means.
est	A 2 x 1 vector of augmented IPW estimates of means.
var	The estimated variances associated with the augmented IPW estimates of means.
ze	The z-statistics for the augmented IPW estimates of means, compared to off.
diff	The augmented IPW estimate of ATE.
diff.var	The estimated variance associated with the augmented IPW estimate of ATE.
diff.ze	The z-statistic for the augmented IPW estimate of ATE.

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.