Address PR #372 R3 review (1 P2, 1 P3)

P2 - StaggeredTripleDifference catalog link points at api/index.html which
does not list it
- Update README.md L116 catalog link from `api/index.html` to
  `api/staggered.html#staggeredtripledifference` (where the autoclass added
  in the prior commit actually renders).
- Sync the same fix in diff_diff/guides/llms.txt L67 so the bundled
  AI-agent guide and the README agree on the canonical URL.

P3 - docs/api/staggered.rst intro counted "two main estimators" but page
now also documents StaggeredTripleDifference (added in prior commit)
- Update intro to list three estimators (CS, SA, StaggeredTripleDifference)
  and clarify the robustness-check note applies to CS+SA only.

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

Author: igerber

README.md

@@ -113,7 +113,7 @@ Full guide: `diff_diff.get_llm_guide("practitioner")`.
 - [StackedDiD](https://diff-diff.readthedocs.io/en/stable/api/stacked_did.html) - Wing, Freedman & Hollingsworth (2024) stacked DiD with Q-weights and sub-experiments
 - [EfficientDiD](https://diff-diff.readthedocs.io/en/stable/api/efficient_did.html) - Chen, Sant'Anna & Xie (2025) efficient DiD with optimal weighting for tighter SEs
 - [TROP](https://diff-diff.readthedocs.io/en/stable/api/trop.html) - Triply Robust Panel estimator (Athey et al. 2025) with nuclear norm factor adjustment
-- [StaggeredTripleDifference](https://diff-diff.readthedocs.io/en/stable/api/index.html) - Ortiz-Villavicencio & Sant'Anna (2025) staggered DDD with group-time ATT
+- [StaggeredTripleDifference](https://diff-diff.readthedocs.io/en/stable/api/staggered.html#staggeredtripledifference) - Ortiz-Villavicencio & Sant'Anna (2025) staggered DDD with group-time ATT
 - [WooldridgeDiD](https://diff-diff.readthedocs.io/en/stable/api/wooldridge_etwfe.html) - Wooldridge (2023, 2025) ETWFE: saturated OLS, logit/Poisson QMLE (ASF-based ATT). Alias `ETWFE`.
 - [BaconDecomposition](https://diff-diff.readthedocs.io/en/stable/api/bacon.html) - Goodman-Bacon (2021) decomposition for diagnosing TWFE bias in staggered settings
 

diff_diff/guides/llms.txt

@@ -64,7 +64,7 @@ Full practitioner guide: call `diff_diff.get_llm_guide("practitioner")`
 - [StackedDiD](https://diff-diff.readthedocs.io/en/stable/api/stacked_did.html): Wing, Freedman & Hollingsworth (2024) stacked DiD with Q-weights and sub-experiments
 - [EfficientDiD](https://diff-diff.readthedocs.io/en/stable/api/efficient_did.html): Chen, Sant'Anna & Xie (2025) efficient DiD with optimal weighting for tighter SEs
 - [TROP](https://diff-diff.readthedocs.io/en/stable/api/trop.html): Triply Robust Panel estimator (Athey et al. 2025) with nuclear norm factor adjustment
-- [StaggeredTripleDifference](https://diff-diff.readthedocs.io/en/stable/api/index.html): Ortiz-Villavicencio & Sant'Anna (2025) staggered DDD with group-time ATT
+- [StaggeredTripleDifference](https://diff-diff.readthedocs.io/en/stable/api/staggered.html#staggeredtripledifference): Ortiz-Villavicencio & Sant'Anna (2025) staggered DDD with group-time ATT
 - [WooldridgeDiD](https://diff-diff.readthedocs.io/en/stable/api/wooldridge_etwfe.html): Wooldridge (2023, 2025) ETWFE — saturated OLS, logit/Poisson QMLE (ASF-based ATT). Alias: ETWFE
 - [BaconDecomposition](https://diff-diff.readthedocs.io/en/stable/api/bacon.html): Goodman-Bacon (2021) decomposition for diagnosing TWFE bias in staggered settings
 

docs/api/staggered.rst

@@ -3,12 +3,13 @@ Staggered Adoption
 
 Estimators for staggered DiD designs where treatment is adopted at different times.
 
-This module provides two main estimators for staggered adoption settings:
+This module provides three estimators for staggered adoption settings:
 
 1. **Callaway-Sant'Anna (2021)**: Aggregates group-time 2x2 DiD comparisons
 2. **Sun-Abraham (2021)**: Interaction-weighted regression approach
+3. **Ortiz-Villavicencio & Sant'Anna (2025)**: Staggered triple-difference (DDD) with group-time ATT
 
-Running both provides a useful robustness check—when they agree, results are more credible.
+Running CS and SA together provides a useful robustness check - when they agree, results are more credible.
 
 .. module:: diff_diff.staggered