mus.AIPW: Calculate weighted mean outcome with AIPW.
In Team-Wang-Lab/T-RL: Tree-based Reinforcement Learning for estimating optimal DTR.
Description
Calculate weighted mean outcome with AIPW.
Usage
1
mus.AIPW(Y, A, pis.hat, mus.reg)
Arguments
Y
A vector of outcome of interest.
A
Treatment vector.
pis.hat
Estimated propensity score matrix.
mus.reg
Regression-based conditional mean outcome.
Team-Wang-Lab/T-RL documentation built on Jan. 3, 2020, 12:11 a.m.
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Description
Calculate weighted mean outcome with AIPW.
Usage
1 | mus.AIPW(Y, A, pis.hat, mus.reg)
|
Arguments
Y |
A vector of outcome of interest. |
A |
Treatment vector. |
pis.hat |
Estimated propensity score matrix. |
mus.reg |
Regression-based conditional mean outcome. |
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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