ate.ipw: Inverse probability weighted estimation of average treatment...

In RCAL: Regularized Calibrated Estimation

Description

This function implements inverse probability weighted (IPW) estimation of average treatment effects (ATEs), provided fitted propensity scores.

Usage

ate.ipw(y, tr, mfp)

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).

Value

one	The direct IPW estimates of 1.
est	The ratio IPW estimates of means.
diff	The ratio 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.