Compute weights for ate estimation
alphaHatEff(Y, X, Z, Qn1, Qn0, gn, select)
| Y | The outcome |
|---|---|
| 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.