Split.X: Split a parent node into two child nodes by a covariate....
In Team-Wang-Lab/T-RL: Tree-based Reinforcement Learning for estimating optimal DTR.
Description
Split a parent node into two child nodes by a covariate.
Split the parent node in order to maximize the outcome of interest by signing different treatment in child nodes. Calculate new outcome means for each child node.
Usage
1
Split.X(X, A, mus.hat, minsplit = 20)
Arguments
X
Covariate matrix.
A
A vector of observed treatments.
mus.hat
Estimated conditional mean outcome.
minsplit
Minimal node size.
Team-Wang-Lab/T-RL documentation built on Jan. 3, 2020, 12:11 a.m.
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Description
Split a parent node into two child nodes by a covariate. Split the parent node in order to maximize the outcome of interest by signing different treatment in child nodes. Calculate new outcome means for each child node.
Usage
1 | Split.X(X, A, mus.hat, minsplit = 20)
|
Arguments
X |
Covariate matrix. |
A |
A vector of observed treatments. |
mus.hat |
Estimated conditional mean outcome. |
minsplit |
Minimal node size. |
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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