twostep.lasso.ate: Refit lasso correction for confounding variables
In swager/balanceHD: Approximately balanced estimation of average treatment effects in high dimensions.
Description
Usage
Arguments
Value
Description
Refit lasso correction for confounding variables
Usage
1
2
twostep.lasso.ate(X, Y, W, target.pop = c(0, 1), fit.propensity = TRUE,
estimate.se = FALSE)
Arguments
X
the input features
Y
the observed responses
W
treatment/control assignment, coded as 0/1
target.pop
which population should the treatment effect be estimated for?
(0, 1): average treatment effect for everyone
0: average treatment effect for controls
1: average treatment effect for treated
fit.propensity
should propensity model be used for variable selection?
Value
ATE estimate
swager/balanceHD documentation built on Aug. 10, 2021, 1:54 a.m.
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Description Usage Arguments Value
Description
Refit lasso correction for confounding variables
Usage
1 2 | twostep.lasso.ate(X, Y, W, target.pop = c(0, 1), fit.propensity = TRUE,
estimate.se = FALSE)
|
Arguments
X |
the input features |
Y |
the observed responses |
W |
treatment/control assignment, coded as 0/1 |
target.pop |
which population should the treatment effect be estimated for? (0, 1): average treatment effect for everyone 0: average treatment effect for controls 1: average treatment effect for treated |
fit.propensity |
should propensity model be used for variable selection? |
Value
ATE estimate
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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