Description Usage Arguments Value
Refit lasso correction for confounding variables
1 2 | twostep.lasso.ate(X, Y, W, target.pop = c(0, 1), fit.propensity = TRUE,
estimate.se = FALSE)
|
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? |
ATE estimate
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