Description Usage Arguments Value
Estimate ATE via inverse propensity weighting
1 2 3 4 5 |
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 |
eps.threshold |
cap on the estimated propensities |
fit.method |
the method used to fit mu(x, w) = E[Y | X = x, W = w] |
alpha.fit |
tuning paramter for glmnet in the mu model |
prop.method |
the method used to fit e(x) = P[W = 1 | X] |
alpha.prop |
tuning paramter for glmnet in the propsenity model |
prop.weighted.fit |
whether propensity weights should be used to as sample weights in outcome fit |
targeting.method |
how to combine the outcome and propensity model fits. |
ATE estimate
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