Address PR #396 R2 review (1 P1)

P1: The "Resolved estimand is not what I expected" inspection snippet
computed `d.min()` over `had_data['dose']` (the full panel column),
but HAD's `_detect_design()` resolves on `D_{g,F}` — per-unit dose at
the first treated period — not the full panel. Per
`diff_diff/had.py:1834-1888` (event-study path) and
`diff_diff/had.py:4089-4091` (`_detect_design(d_arr)` where `d_arr`
is `D_{g,F}`), the detector ignores the structural pre-period zeros
that HAD requires (`D_{g,t} = 0` for `t < F`). Consequence: on every
valid HAD panel, `had_data['dose'].min()` is always 0 and the
snippet would report "Design 1' (WAS)" regardless of the true
resolution — exactly the false sense of confirmation the
troubleshooting page is meant to dispel.

Fix: rewrote the snippet to extract `d_at_F = had_data.loc[
had_data['period'] == F].set_index('unit')['dose']` (per-unit
post-period dose, mirroring `had.py:1886-1888`) and compute the
threshold check on that series. Renamed printed labels from
`d.min()` to `D_{g,F}.min()` and `0.01 * median(|d|)` to
`0.01 * median(|D_{g,F}|)` so the diagnostic syntactically matches
the registry's rule statement.

Updated the surrounding **Cause** prose at `docs/troubleshooting.rst
:486-495` to (a) state explicitly that detection runs on `D_{g,F}`
not the panel column, (b) note that pre-period zeros on the panel
column are structural and uninformative for design choice, and (c)
restate the threshold rule and modal-fraction check on `D_{g,F}`.

Verification: PYTHONPATH=. DIFF_DIFF_BACKEND=python pytest
tests/test_doc_snippets.py reports 111 passed, 4 skipped, 0 failed.

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

Author: igerber

docs/troubleshooting.rst

@@ -483,16 +483,20 @@ HeterogeneousAdoptionDiD (HAD) Issues
 **Problem:** ``HeterogeneousAdoptionDiD`` resolves ``target_parameter`` to
 ``"WAS_d_lower"`` when you expected ``"WAS"`` (or vice versa).
 
-**Cause:** HAD auto-detects the design path from the dose distribution. The
-``_detect_design`` rule resolves to Design 1' (``continuous_at_zero``,
-targets WAS) when EITHER ``d.min() == 0`` exactly OR ``d.min()`` is a small
-positive value below ``0.01 * median(|d|)`` (the small-share-of-treated
-escape clause). Otherwise (``d.min()`` larger than that threshold) the
-estimator routes to Design 1, with a further check for mass-point structure
-(modal fraction at ``d.min()`` exceeding 2% routes to ``mass_point``;
-otherwise ``continuous_near_d_lower``); both Design 1 paths target
-``WAS_{d_lower}``. So a Design 1 resolution only fires when ``d.min()``
-is meaningfully positive relative to the dose scale.
+**Cause:** HAD auto-detects the design path from the unit-level
+post-treatment dose ``D_{g,F}`` (the dose at the first treated period
+``F``, one value per unit), NOT from the full panel ``dose`` column. The
+panel column carries structural pre-period zeros (HAD requires
+``D_{g,t} = 0`` for ``t < F``), so ``had_data['dose'].min()`` is always
+zero on a valid HAD panel and tells you nothing about the resolved
+design. ``_detect_design`` then resolves on ``D_{g,F}`` and picks Design
+1' (``continuous_at_zero``, targets WAS) when EITHER
+``D_{g,F}.min() == 0`` exactly OR ``D_{g,F}.min()`` is a small positive
+value below ``0.01 * median(|D_{g,F}|)`` (the small-share-of-treated
+escape clause). Otherwise the estimator routes to Design 1, with a
+further check for mass-point structure (modal fraction at ``D_{g,F}.min()``
+exceeding 2% routes to ``mass_point``; otherwise
+``continuous_near_d_lower``); both Design 1 paths target ``WAS_{d_lower}``.
 
 **Solutions:**
 
@@ -516,12 +520,16 @@ is meaningfully positive relative to the dose scale.
            rows.append({'unit': g, 'period': t, 'y': y, 'dose': d})
    had_data = pd.DataFrame(rows)
 
-   # Inspect the dose support before fitting
-   d = had_data['dose'].to_numpy()
-   print(had_data['dose'].describe())
-   print(f"d.min() = {d.min():.6g}; "
-         f"0.01 * median(|d|) = {0.01 * np.median(np.abs(d)):.6g}; "
-         f"d.min() < threshold => Design 1' (WAS)")
+   # Inspect the support the detector actually uses: per-unit dose at the
+   # first treated period F. Pre-period zeros on the panel column are
+   # structural and ignored by `_detect_design()`.
+   d_at_F = had_data.loc[had_data['period'] == F].set_index('unit')['dose']
+   print(d_at_F.describe())
+   d_min = float(d_at_F.min())
+   d_thr = 0.01 * float(np.median(np.abs(d_at_F)))
+   print(f"D_{{g,F}}.min() = {d_min:.6g}; "
+         f"0.01 * median(|D_{{g,F}}|) = {d_thr:.6g}; "
+         f"D_{{g,F}}.min() < threshold => Design 1' (WAS)")
 
    # Check the resolved estimand after fitting
    est = HeterogeneousAdoptionDiD()
@@ -530,9 +538,9 @@ is meaningfully positive relative to the dose scale.
                      aggregate='event_study')
    print(f"Resolved: {results.target_parameter}")
 
-   # If you intend Design 1' but `d.min()` exceeds the threshold, verify
-   # the dose-variable encoding (e.g. log-transformed doses where 0 was
-   # mapped to a small positive value larger than 1% of the median).
+   # If you intend Design 1' but `D_{g,F}.min()` exceeds the threshold,
+   # verify the dose-variable encoding (e.g. log-transformed doses where
+   # 0 was mapped to a small positive value larger than 1% of the median).
 
 "Mass-point design selected"
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~