Featurizer for ForestDrLearner, and in nuisance models

Hi,
Thank you for all the work you do. I have some concept questions if contributors would like to weigh in:

1. Is there intuition for why featurizer is not implemented for the ForestDRLearner (but is for the other DRLearners?) Is that because featurizers were introduced to add flexible interactions for the linear methods, so were thought to be redundant for the more flexible forest methods?

2. I ask this question because I want to bring some models into production and so am hoping to move some data processing and feature engineering to a scikit-learn Pipeline (median imputation, one-hot encoding). If transitioning the nuisance models to a Pipeline, would it be at all feasible to pass, e.g., features with null values as W?

3. Relatedly, is there thought to be a best practice for handling imputation in X (treatment effect modifiers) - beyond removing features that require imputation?

The issue names ForestDRLearner, other DRLearners, nuisance models, and scikit-learn Pipeline. Start by comparing the learner entry points and how nuisance models receive features; the scope and completion criteria would need to be clarified before implementation.