Fix E306 lint error and update changelog for unadjusted outcome-model summary

The previous commit removed the unconditional aipw() call from
_outcome_estimates_summary(), which left the nested _fmt_points
definition with no preceding blank line (flake8 E306). Add it back.

Also drop the '(see the changelog)' reference from the comment per
review: the changelog summary section now explicitly documents that
mu_DR/AIPW appears only when the frame is adjust()-calibrated,
matching the new summary() behavior.

Author: rootkiller6788

CHANGELOG.md

@@ -113,10 +113,12 @@ for the design rationale.
     weights are constant (`μ̂_DR` reduces to `μ̂_OM`). **Point estimate only** — no
     CI yet; see the TODOs in `balance/outcome_models/aipw.py`.
 
-- **`summary()` reports the estimator trio** when a fitted outcome model and a
-  target are both present: an "Outcome estimates" section with `μ̂_IPW` and its
-  analytic CI, plus `μ̂_OM` and `μ̂_DR` as point estimates. **With no outcome model
-  fit, `summary()` output is unchanged.** Separately,
+- **`summary()` reports outcome-model estimates** when a fitted outcome model
+  and a target are both present: an "Outcome estimates" section with `μ̂_IPW` and
+  its analytic CI, plus `μ̂_OM` as a point estimate, and `μ̂_DR` when the frame is
+  `adjust()`-calibrated (its AIPW block needs those weights; an unadjusted frame
+  with a fitted model is a supported workflow and omits it). **With no outcome
+  model fit, `summary()` output is unchanged.** Separately,
   `outcomes_hat().summary()` **scopes any doubly-robust claim to the fit weights**
   — a linear learner with an intercept fit with non-uniform weights reports
   `"doubly robust w.r.t. weights <col>"`; everything else reports plain

balance/balance_frame.py

@@ -3752,6 +3752,7 @@ def _outcome_estimates_summary(self) -> str | None:
             for col in model["outcome_columns"]
             if f"{col}_hat" in target_predictions.columns
         }
+
         def _fmt_points(estimates: dict[str, float]) -> str:
             return "\n".join(
                 f"    {col}    {val:.3f}" for col, val in estimates.items()
@@ -3772,8 +3773,8 @@ def _fmt_points(estimates: dict[str, float]) -> str:
 
         # μ̂_DR requires adjust()-calibrated responder weights on the same
         # population scale as the target (``aipw()`` enforces this). A frame
-        # with a fitted outcome model but no adjust() is a supported workflow
-        # (see the changelog), so omit the AIPW block instead of crashing.
+        # with a fitted outcome model but no adjust() is a supported workflow,
+        # so omit the AIPW block instead of crashing.
         if self.is_adjusted:
             dr_estimates = {str(col): float(v) for col, v in self.aipw().items()}
             blocks.extend(