Add return_components support to R-learner

[aman-coder03]

Proposed changes

this PR adds return_components support to the R-Learner, bringing its API in line with the existing T- and X-Learner implementations

specific changes..

• adds a return_components argument to predict() and fit_predict() for both BaseRLearner and BaseRClassifier
• returns the nuisance components used by the R-Learner
  • yhat: outcome model predictions (E[Y|X])
  • p: propensity score estimates (E[W|X])
• adds the same mutual exclusion guard as other meta-learners, preventing return_ci and return_components from being used together
• fits the nuisance outcome model after cross-validation so that it can be used for inference-time component retrieval.
• adds tests covering the new return_components functionality for both predict() and fit_predict() along with the mutual exclusion behavior

fixes #304

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[jeongyoonlee]

Two blockers before merge:

B1 — the p component is the stored training propensity, not recomputed for the passed X.
In both predict() bodies, when p is None you set p = self.propensity (training-data propensity) while yhat = self.model_mu.predict(X) is computed for the passed X. The two returned components end up on different bases, and on a differently-sized X the p array length won't match yhat/te — with no error raised:

rl.fit(X[:800], treatment[:800], y[:800], p=p[:800])
te, yhat, p_hat = rl.predict(X[800:1000], return_components=True)
# te: (200, 1), yhat: len 200, p_hat: len 800

Please mirror the X-learner, which recomputes propensity for X (xlearner.py:203/:643):

if p is None:
    p = {g: self.propensity_model[g].predict(X) for g in self.t_groups}
else:
    p = self._format_p(p, self.t_groups)

Note self.propensity_model only exists when fit() ran with p=None; if the user supplied p at fit and then calls predict(p=None) there is no model to recompute from — raise a clear error there rather than returning stale training values. The current test doesn't surface this because it always passes p=p_scores and predicts on the training X of equal length.

B2 — predict(..., return_ci=True) is accepted but never implemented.
The new predict() signature adds return_ci=False, but the body only uses it for the return_ci/return_components mutual-exclusion guard — it never computes CIs. So te, lb, ub = rl.predict(X, return_ci=True) raises ValueError at the unpack site (same footgun as #886). R-learner has no per-predict bootstrap path, so rather than accept-and-ignore, please drop return_ci from predict() (keep the guard in fit_predict, which does implement it) or raise NotImplementedError when True.

Tests: the new coverage only exercises BaseRLearner. Since BaseRClassifier and XGBRRegressor both had fit()/predict() modified, please add return_components tests for the classifier override and XGBR, plus the p=None predict path and a predict on a different-sized X (that last one guards B1).

Non-blocking notes to follow separately.

[jeongyoonlee]

One blocker:

predict(X) (with return_components off) now raises on a learner fit with an explicit p.
The nuisance computation was hoisted above the te-only early return, so p (and yhat) are computed on every predict() even when return_components=False. On master predict(X, p=None) never touches p — the CATE comes only from models_tau — so this is a regression:

rl = BaseRRegressor(learner=LinearRegression(), control_name="control")
rl.fit(X, treatment, y, p=p)   # fit with explicit propensity
rl.predict(X)                  # master: returns te;  this PR: ValueError: No propensity model is available.

That's why get_synthetic_preds_holdout and three existing tests had to start passing p= to predict(). Please move the p/yhat computation inside if return_components: so the plain predict(X) → te path stays as on master (and skips the extra work):

te = np.zeros((n_rows(X), self.t_groups.shape[0]))
for i, group in enumerate(self.t_groups):
    te[:, i] = self.models_tau[group].predict(X)
if not return_components:
    return te
if p is None:
    if not hasattr(self, "propensity_model"):
        raise ValueError("No propensity model is available. ...")
    p = {g: self.propensity_model[g].predict(X) for g in self.t_groups}
else:
    p = self._format_p(p, self.t_groups)
yhat = self.model_mu.predict(X)
return te, yhat, p

Same change in the BaseRClassifier override. With that, the p= additions in synthetic.py and the tests become unnecessary.

Non-blocking notes to follow separately.

[jeongyoonlee]

LGTM.

One non-blocking follow-up: self.model_mu.fit(X, y_np) runs on every fit() even when return_components is never used — worth gating or lazy-fitting so the extra full-data fit only happens when the components are actually requested.