Add propensity_learner to R-learner.

[jeongyoonlee]

Proposed changes

Since propensity modeling takes an important role in R-learner, it'd be better if users can define which learner to use for propensity modeling. This PR includes:

• add propensity_learner as an input argument to BaseRLearner with ElasticNetPropensityModel as default.
• refactor R/X-learners by defining _set_propensity_models() and _format_p() in BaseLearner.

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• New feature (non-breaking change which adds functionality)
• Breaking change (fix or feature that would cause existing functionality to not work as expected)
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Further comments

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[ppstacy]

Hi @jeongyoonlee a quick question that might not be directly related to this PR. Why do we need two treatment and treatment_pred here? Aren't those the same thing based on here: https://github.com/uber/causalml/blob/master/causalml/propensity.py#L189?

[jeongyoonlee]

In this call to compute_propensity_score(), treatment is only for the filtered samples with treatment belonging to either the control group or treatment group of interest while treatment_pred is for the entire samples. These two will be the same for a single treatment, but different for the multiple treatments.

[ppstacy]

Gotcha I see. Sorry a follow-up question here, why do we need to calibrate it with the entire dataset? I am asking because we are only using treatment group X + control when running the model in https://github.com/uber/causalml/blob/master/causalml/inference/meta/rlearner.py#L111-L118.

[ppstacy]

LGTM, thanks!