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Confused about dimensionality of nuisance model predictions #342

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Hello @RandallJEllis

thanks for your interesting question.
By design and for consistency, DoubleML iterates over all treatments and fits all nuisance models for each treatment. This happens even if a nuisance target is independent of the individual treatments. This explains the dimensionality.

Since you use data = DoubleMLData.from_arrays(x=Z, y=Y, d=T) the default value use_other_treat_as_covariate=True is set and thus, for each treatment, all other 49 treatments join the set of covariates when estimating ml_l and ml_m.

If you'd like to set use_other_treat_as_covariate=False and avoid recalculating ml_l, you can use the doubleml samples to create one set of predictions, broadcast them on…

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@RandallJEllis
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