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Issue on page /22-Debiased-Orthogonal-Machine-Learning.html #363
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WIth out saving the first stage it is impossible to use non-functional form
to create the measure of the probability of something occurring it is an
extension of the sharp null ideaz.
[image: image.png]
…On Mon, Nov 13, 2023 at 4:39 AM Sebastian Krantz ***@***.***> wrote:
I fail to understand why in the section "Non-Scientific Double/Debiased
ML" it is necessary to save the first stage models and predict with them.
In adding counterfactual treatments, we are not changing any part of the
covariates X which are the sole input to the first stage models. Thus the
first-stage predictions are the same with or without counterfactual
treatments and we don't need those models.
In addition, I don't quite understand the value of training and test
splitting and the ensamble_pred() function here. If my goal is to get
counterfactual predictions for all my data (which typically is the case), I
would just use cross_val_predict() to get the first stage residuals (as
in the section on DML), and then fit cross-validated final models using
cv_estimate(), additionally saving the indices for each fold, and then
create a predict method that uses the final-stage models and indices to
create proper cross-validated final predictions for different price levels
(subtracted their prediction from the first stage, which remains the same).
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I fail to understand why in the section "Non-Scientific Double/Debiased ML" it is necessary to save the first stage models and predict with them. In adding counterfactual treatments, we are not changing any part of the covariates X which are the sole input to the first stage models. Thus the first-stage predictions are the same with or without counterfactual treatments and we don't need those models.
In addition, I don't quite understand the value of training and test splitting and the
ensamble_pred()
function here. If my goal is to get counterfactual predictions for all my data (which typically is the case), I would just usecross_val_predict()
to get the first stage residuals (as in the section on DML) on the entire data, and then fit cross-validated final models usingcv_estimate()
, additionally saving the indices for each fold, and then create a predict method that uses the final-stage models and indices to create proper cross-validated final predictions for different price levels (subtracted their prediction from the first stage, which remains the same).The text was updated successfully, but these errors were encountered: