Address PR #356 CI review round 9 (1 P1 + 1 P2 semantic)

Rebased onto current main (resolved CHANGELOG.md conflict: the by_path
bullet from PR #355 and the profile_panel/autonomous-guide bullet from
this PR now live side-by-side under [Unreleased]).

has_always_treated now has binary-only semantics:
- For binary treatment (absorbing or non-absorbing): unit_min == 1
  means the unit is treated in every observed period (no
  pre-treatment information in the DiD sense).
- For continuous treatment: always False. Pre-treatment periods on
  continuous DiD are determined by the separate `first_treat` column
  supplied to `ContinuousDiD.fit`, not by whether the dose is
  positive. A unit with a constant positive dose can still have
  well-defined pre-treatment periods, so flagging it as
  "always-treated / no pre-treatment information" was factually wrong
  and triggered the misleading `has_always_treated_units` alert on
  valid continuous panels.
- Categorical: False by construction.

Guide §2 has_always_treated field doc updated to state the
binary-only semantics explicitly, with a note about `first_treat`.

Tests:
- New: test_continuous_positive_dose_does_not_fire_has_always_treated
  asserts has_always_treated=False AND the alert does not fire on a
  constant-positive-dose continuous panel.
- Existing test_continuous_zero_dose_controls_flag_has_never_treated
  updated: has_always_treated expected to be False (was True under
  the old semantics).

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

Author: igerber

diff_diff/guides/llms-autonomous.txt

@@ -114,10 +114,15 @@ view. Every field below appears as a top-level key in that dict.
   is not yet implemented). Preferred-but-optional by
   `CallawaySantAnna` and `ChaisemartinDHaultfoeuille`. Always `False`
   for `"categorical"`.
-- **`has_always_treated: bool`** - at least one unit has
-  strictly-positive treatment in every observed non-NaN row. Such units
-  provide no pre-treatment identification and are dropped by most
-  estimators. Always `False` for `"categorical"`.
+- **`has_always_treated: bool`** - at least one binary-treatment
+  unit has `treatment == 1` in every observed non-NaN row (no
+  pre-treatment information for that unit in the DiD sense).
+  Binary-only semantics: for `"continuous"` panels this field is
+  always `False` because pre-treatment periods are determined by the
+  `first_treat` column supplied to `ContinuousDiD.fit()`, not by
+  whether the dose is positive - a unit with a constant positive dose
+  can still have well-defined pre-treatment periods. Always `False`
+  for `"categorical"` too.
 - **`treatment_varies_within_unit: bool`** - at least one unit has more
   than one distinct non-NaN treatment value across its observed rows.
   For binary panels this is normally `True` (pre vs. post the adoption

diff_diff/profile.py

@@ -353,16 +353,25 @@ def _classify_treatment(
     if n_distinct == 0:
         return ("categorical", False, {}, False, False, None, None)
 
-    # Generic never-/always-treated semantics (applies to both binary
-    # and continuous numeric treatment): "never-treated" means the unit
-    # has treatment == 0 in every observed non-NaN row; "always-treated"
-    # means treatment > 0 in every observed non-NaN row.
+    # has_never_treated has a single well-defined meaning across binary
+    # and continuous numeric treatment: some unit has treatment == 0 in
+    # every observed non-NaN row. For binary this is the clean-control
+    # group; for continuous this is the zero-dose control required by
+    # ContinuousDiD (P(D=0) > 0).
     unit_max = df.groupby(unit)[treatment].max().to_numpy()
     unit_min = df.groupby(unit)[treatment].min().to_numpy()
     has_never_treated = bool(np.any(unit_max == 0))
-    has_always_treated = bool(np.any(unit_min > 0))
 
     is_binary_valued = values_set <= {0, 1, 0.0, 1.0}
+    # has_always_treated has binary-only semantics: "unit is treated in
+    # every observed period" = unit_min == 1 on a binary panel (no
+    # pre-treatment information). For continuous panels, positive dose
+    # throughout does not mean "always treated in the DiD sense"
+    # (pre-treatment periods are determined by `first_treat`, not by
+    # whether the dose is positive), so this field is False for
+    # continuous / categorical types.
+    has_always_treated = is_binary_valued and bool(np.any(unit_min == 1))
+
     if not is_binary_valued:
         return (
             "continuous",

tests/test_profile_panel.py

@@ -129,6 +129,32 @@ def test_binary_absorbing_varies_within_unit():
     assert profile.treatment_varies_within_unit is True
 
 
+def test_continuous_positive_dose_does_not_fire_has_always_treated():
+    """Valid ContinuousDiD panels have units with a constant positive
+    dose across all periods AND well-defined pre-treatment periods
+    (via a separate `first_treat` column). `has_always_treated` has
+    binary-only semantics, so it must be False on continuous panels
+    regardless of dose positivity. Previously the field conflated
+    "positive dose throughout" with "always treated in the DiD sense",
+    which fired the misleading `has_always_treated_units` alert on
+    valid continuous-DiD panels."""
+    rng = np.random.default_rng(0)
+    rows = []
+    for u in range(1, 21):
+        dose = 0.0 if u <= 5 else 2.5
+        for t in range(4):
+            rows.append({"u": u, "t": t, "tr": dose, "y": rng.normal()})
+    df = pd.DataFrame(rows)
+    profile = profile_panel(df, unit="u", time="t", treatment="tr", outcome="y")
+    assert profile.treatment_type == "continuous"
+    assert profile.has_never_treated is True
+    assert profile.has_always_treated is False, (
+        "has_always_treated must be False on continuous panels regardless "
+        "of dose positivity (binary-only semantics)"
+    )
+    assert "has_always_treated_units" not in _alert_codes(profile)
+
+
 def test_categorical_treatment_object_dtype():
     rows = []
     for u in range(1, 11):
@@ -386,7 +412,9 @@ def test_reversal_through_nan_is_binary_non_absorbing():
 def test_continuous_zero_dose_controls_flag_has_never_treated():
     """Continuous treatment with some zero-dose units must flag
     has_never_treated=True. Previously continuous panels hardcoded
-    has_never_treated=False regardless of control availability."""
+    has_never_treated=False regardless of control availability.
+    has_always_treated has binary-only semantics and must remain
+    False on continuous panels regardless of dose positivity."""
     rows = []
     rng = np.random.default_rng(0)
     for u in range(1, 21):
@@ -397,7 +425,7 @@ def test_continuous_zero_dose_controls_flag_has_never_treated():
     profile = profile_panel(df, unit="u", time="t", treatment="tr", outcome="y")
     assert profile.treatment_type == "continuous"
     assert profile.has_never_treated is True
-    assert profile.has_always_treated is True
+    assert profile.has_always_treated is False
 
 
 def test_guide_api_strings_resolve_against_public_api():