rlearner_score still requires outcome_learner while its siblings default to LightGBM

[jeongyoonlee]

#1030 gave compute_dr_pseudo_outcomes (cate_scoring.py:63), dr_score (:275) and plug_in_t_score (:399) a default LGBMRegressor(num_leaves=64, learning_rate=0.05, n_estimators=300, verbose=-1). rlearner_score (:499) was not touched, so outcome_learner (:506) is still required:

from causalml.metrics import rlearner_score
rlearner_score(df, X=X, outcome_col="y", treatment_col="w")
# AssertionError: Either `y_residual_col`/`w_residual_col` (present in df) or `X` and
# `outcome_learner` (to compute residuals internally) must be provided.

The rationale in #1028 — that the sibling metrics in causalml.metrics default to LightGBM — applies to rlearner_score identically, and after #1030 the module is inconsistent with itself.

Two changes, both in causalml/metrics/cate_scoring.py:

1. Give outcome_learner (:506) the same signature default as its three siblings.
2. Replace the bare asserts at :567 and :582 with ValueError. Asserts are stripped under python -O, so the guard silently disappears and the failure resurfaces later as a confusing error inside the residual computation. (dr_score and plug_in_t_score have the same pattern at :350, :364 and :452 — worth converting in the same pass.)

Scope: one module. No behavior change for callers who already pass outcome_learner.

Observed on master (477cd0a).

🤖 Generated with Claude Code

[su-jin1425]

I will start working on 28/Aug