Validating model via predictions about causalml HOT 2 OPEN

It's expected that the T-Learner returns a number of control vectors corresponding to the number of treatments. This is because the implementation simply loops over each treatment and estimates a separate model for it. So the yhat_cs are the predicted control outcomes from each of those separate models. To get the predicted control outcome for the units in each of the three treatment groups, you need to apply a similar masking as in the code snippet that you provided, for each of the three control vectors. So, for the units in the first treatment group, you select the control observations from the first control vector. And so on for the remaining pairs.

from causalml.

Thanks for your response, @t-tte. I see that I can generate predictions for each of the treatment groups and even for the control group for the T-learner, but it looks like I cannot generate predictions for the control group for other meta-learners (although I can generate predictions for the treatment groups for all but R-learners). Is it possible to generate predictions for outcomes for the treatment groups for an uplift tree? That is, I'd rather have the prediction for the raw outcome for each treatment than an uplift score for each treatment so that I can compare to observed values.

from causalml.