Update bundled LLM guides for dCDH survey + SDID jackknife

- diff_diff/guides/llms-practitioner.txt: add reversible-treatment branch
  to the Step 4 decision tree pointing at ChaisemartinDHaultfoeuille
  (alias DCDH), and a short Step 5 example showing the
  survey_design + L_max + TSL fallback for dCDH. The practitioner guide
  previously had zero dCDH coverage despite the full API being live.
- diff_diff/guides/llms-full.txt: fix stale SyntheticDiDResults table
  (line 1024) — variance_method now accepts "bootstrap", "jackknife",
  or "placebo" (jackknife was added for synthdid::vcov parity but the
  table only listed bootstrap/placebo).

Bundled-guide paths only. The old docs/llms-*.txt were moved to
diff_diff/guides/ in main (PR #310); these edits target the new
canonical location consumed by get_llm_guide().

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

Author: igerber

diff_diff/guides/llms-full.txt

@@ -1021,7 +1021,7 @@ Returned by `SyntheticDiD.fit()`.
 | `time_weights` | `dict` | Pre-treatment time weights |
 | `pre_periods` | `list` | Pre-treatment periods |
 | `post_periods` | `list` | Post-treatment periods |
-| `variance_method` | `str` | "bootstrap" or "placebo" |
+| `variance_method` | `str` | "bootstrap", "jackknife", or "placebo" |
 | `noise_level` | `float` | Estimated noise level |
 | `zeta_omega` | `float` | Unit weight regularization |
 | `zeta_lambda` | `float` | Time weight regularization |

diff_diff/guides/llms-practitioner.txt

@@ -173,6 +173,12 @@ Is treatment adoption staggered (multiple cohorts, different timing)?
 |-- NO, simple 2x2 design:
 |   \-- DifferenceInDifferences (DiD)
 |
+|-- Treatment switches ON and OFF (reversible / non-absorbing)?
+|   \-- ChaisemartinDHaultfoeuille (dCDH / alias `DCDH`)
+|       -- Only library estimator for non-absorbing treatments; supports
+|          L_max multi-horizon, dynamic placebos, cost-benefit delta,
+|          HonestDiD, and `survey_design=` (pweight + strata/PSU/FPC via TSL)
+|
 |-- Few treated units (< 20)?
 |   \-- SyntheticDiD (SDiD)    -- synthetic control + DiD hybrid
 |
@@ -260,6 +266,27 @@ results = es.fit(data, outcome='y', unit='unit_id', time='period',
 print(results.summary())
 ```
 
+### Reversible (non-absorbing) treatment with survey design
+Use `ChaisemartinDHaultfoeuille` (dCDH) when treatment switches ON and OFF.
+Pass `survey_design=SurveyDesign(...)` for design-based inference via Taylor
+Series Linearization. Only `weight_type='pweight'` is supported; replicate
+weights are deferred. When combined with `n_bootstrap > 0`, dCDH emits a
+`UserWarning` and falls back to group-level multiplier bootstrap — prefer
+the analytical TSL path for survey-aware inference.
+
+```python
+from diff_diff import ChaisemartinDHaultfoeuille, SurveyDesign
+
+sd = SurveyDesign(weights='pw', strata='stratum', psu='cluster', nest=True)
+results = ChaisemartinDHaultfoeuille().fit(
+    data, outcome='y', group='unit_id', time='period',
+    treatment='treated',
+    L_max=3,                  # multi-horizon event study
+    survey_design=sd,         # survey-aware analytical SE (TSL)
+)
+print(results.summary())
+```
+
 ---
 
 ## Step 6: Sensitivity Analysis