Compute weights for ate estimation
alphaHatEff(Y, X, Z, Qn1, Qn0, gn, select)
Y | The outcome |
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X | The covariates |
Z | The binary treatment |
Qn1 | A matrix of predictions from SuperLearner on training data with Z = 1 |
Qn0 | A matrix of predictions from SuperLearner on training data with Z = 0 |
gn | A matrix of propensity estimates in training data |
select | How to choose amongst outcomes |
weights A numeric vector of weights of the same length as the number of outcomes considered. The weights sum to 1.