Address PR #199 re-review: scope equivalence phrases, fix citations

- Scope "same point estimates as CS" → "matching post-treatment ATT(g,t) with CS"
- Scope "CS-equivalent" → "post-treatment CS match" in code comment
- Remove "*Working Paper*" suffix from citations to match REGISTRY.md

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

Author: igerber

docs/api/efficient_did.rst

@@ -27,7 +27,7 @@ This module implements the efficiency-bound-attaining estimator that:
 - You need a formal efficiency benchmark for comparing estimators
 
 **Reference:** Chen, X., Sant'Anna, P. H. C., & Xie, H. (2025). Efficient
-Difference-in-Differences and Event Study Estimators. *Working Paper*.
+Difference-in-Differences and Event Study Estimators.
 
 .. module:: diff_diff.efficient_did
 

docs/choosing_estimator.rst

@@ -238,7 +238,7 @@ Use :class:`~diff_diff.EfficientDiD` when:
 
    from diff_diff import EfficientDiD
 
-   edid = EfficientDiD(pt_assumption="all")  # or "post" for CS-equivalent
+   edid = EfficientDiD(pt_assumption="all")  # or "post" for post-treatment CS match
    results = edid.fit(data, outcome='y', unit='unit_id',
                       time='period', first_treat='first_treat',
                       aggregate='all')

docs/tutorials/15_efficient_did.ipynb

@@ -63,21 +63,7 @@
    "cell_type": "markdown",
    "id": "4d734cd9",
    "metadata": {},
-   "source": [
-    "## What Makes EDiD Different?\n",
-    "\n",
-    "Consider a staggered adoption design with cohorts treated at periods 3, 5, and 7, plus a never-treated group. To estimate ATT(g=5, t=6), **Callaway-Sant'Anna** uses a single 2x2 comparison:\n",
-    "\n",
-    "> *Compare the outcome change from period 4 to 6 for cohort 5 versus the never-treated group.*\n",
-    "\n",
-    "But under **PT-All** (parallel trends across all pre-treatment periods), there are *additional* valid comparisons. Cohort 7 is also untreated at period 6, so it can serve as a comparison group too. And periods 1, 2, 3 can all serve as valid baselines, not just period 4.\n",
-    "\n",
-    "Each of these comparisons provides an unbiased estimate of ATT(g=5, t=6), but with different variances. **EDiD finds the optimal linear combination** --- the one that minimizes variance --- by computing the inverse covariance matrix of these \"generated outcomes\" (the paper calls this $\\Omega^*$).\n",
-    "\n",
-    "The result: **same point estimates as CS under PT-Post**, but **tighter standard errors under PT-All** because EDiD exploits the overidentification.\n",
-    "\n",
-    "> **Key equation (for the curious):** The efficient weight vector is $w^* = \\frac{\\mathbf{1}' \\Omega^{*-1}}{\\mathbf{1}' \\Omega^{*-1} \\mathbf{1}}$, where $\\Omega^*$ is the covariance matrix of the generated outcomes across all valid (comparison group, baseline) pairs. This is the classic GLS optimal weighting. See REGISTRY.md or the paper for full derivations."
-   ]
+   "source": "## What Makes EDiD Different?\n\nConsider a staggered adoption design with cohorts treated at periods 3, 5, and 7, plus a never-treated group. To estimate ATT(g=5, t=6), **Callaway-Sant'Anna** uses a single 2x2 comparison:\n\n> *Compare the outcome change from period 4 to 6 for cohort 5 versus the never-treated group.*\n\nBut under **PT-All** (parallel trends across all pre-treatment periods), there are *additional* valid comparisons. Cohort 7 is also untreated at period 6, so it can serve as a comparison group too. And periods 1, 2, 3 can all serve as valid baselines, not just period 4.\n\nEach of these comparisons provides an unbiased estimate of ATT(g=5, t=6), but with different variances. **EDiD finds the optimal linear combination** --- the one that minimizes variance --- by computing the inverse covariance matrix of these \"generated outcomes\" (the paper calls this $\\Omega^*$).\n\nThe result: **matching post-treatment ATT(g,t) with CS under PT-Post**, but **tighter standard errors under PT-All** because EDiD exploits the overidentification.\n\n> **Key equation (for the curious):** The efficient weight vector is $w^* = \\frac{\\mathbf{1}' \\Omega^{*-1}}{\\mathbf{1}' \\Omega^{*-1} \\mathbf{1}}$, where $\\Omega^*$ is the covariance matrix of the generated outcomes across all valid (comparison group, baseline) pairs. This is the classic GLS optimal weighting. See REGISTRY.md or the paper for full derivations."
   },
   {
    "cell_type": "markdown",
@@ -499,7 +485,7 @@
    "cell_type": "markdown",
    "id": "ef99ee47",
    "metadata": {},
-   "source": "## Summary\n\n**Key takeaways:**\n\n1. EDiD achieves the **semiparametric efficiency bound** for ATT estimation in staggered designs\n2. Under **PT-All**, EDiD exploits overidentification for tighter SEs than CS\n3. Under **PT-Post**, EDiD matches CS for post-treatment ATT(g,t); pre-treatment diagnostics use a fixed baseline and may differ from CS's default varying baseline\n4. The efficiency gain comes from optimally weighting across all valid (comparison group, baseline) pairs\n5. **Event study** and **group** aggregations work just like CS\n6. **Multiplier bootstrap** provides robust inference with Rademacher, Mammen, or Webb weights\n7. **Condition numbers** flag potentially unstable weight matrices\n8. **Anticipation** shifts the effective treatment boundary for pre-treatment effects\n9. Phase 1 is **no-covariates only** --- Phase 2 will add covariate support\n10. When in doubt, run both EDiD and CS --- if ATTs agree, report EDiD for tighter CIs\n\n**Parameter reference:**\n\n| Parameter | Default | Description |\n|-----------|---------|-------------|\n| `pt_assumption` | `\"all\"` | `\"all\"` (overidentified) or `\"post\"` (just-identified, matches CS post-treatment ATT) |\n| `alpha` | `0.05` | Significance level |\n| `n_bootstrap` | `0` | Number of bootstrap iterations (0 = analytical only) |\n| `bootstrap_weights` | `\"rademacher\"` | Bootstrap weight distribution: `\"rademacher\"`, `\"mammen\"`, `\"webb\"` |\n| `seed` | `None` | Random seed for reproducibility |\n| `anticipation` | `0` | Anticipation periods |\n\n**Reference:** Chen, X., Sant'Anna, P. H. C., & Xie, H. (2025). Efficient Difference-in-Differences and Event Study Estimators. *Working Paper*.\n\n*See also: [Choosing an Estimator](../choosing_estimator.rst) for guidance on when to use EDiD vs other estimators.*"
+   "source": "## Summary\n\n**Key takeaways:**\n\n1. EDiD achieves the **semiparametric efficiency bound** for ATT estimation in staggered designs\n2. Under **PT-All**, EDiD exploits overidentification for tighter SEs than CS\n3. Under **PT-Post**, EDiD matches CS for post-treatment ATT(g,t); pre-treatment diagnostics use a fixed baseline and may differ from CS's default varying baseline\n4. The efficiency gain comes from optimally weighting across all valid (comparison group, baseline) pairs\n5. **Event study** and **group** aggregations work just like CS\n6. **Multiplier bootstrap** provides robust inference with Rademacher, Mammen, or Webb weights\n7. **Condition numbers** flag potentially unstable weight matrices\n8. **Anticipation** shifts the effective treatment boundary for pre-treatment effects\n9. Phase 1 is **no-covariates only** --- Phase 2 will add covariate support\n10. When in doubt, run both EDiD and CS --- if ATTs agree, report EDiD for tighter CIs\n\n**Parameter reference:**\n\n| Parameter | Default | Description |\n|-----------|---------|-------------|\n| `pt_assumption` | `\"all\"` | `\"all\"` (overidentified) or `\"post\"` (just-identified, matches CS post-treatment ATT) |\n| `alpha` | `0.05` | Significance level |\n| `n_bootstrap` | `0` | Number of bootstrap iterations (0 = analytical only) |\n| `bootstrap_weights` | `\"rademacher\"` | Bootstrap weight distribution: `\"rademacher\"`, `\"mammen\"`, `\"webb\"` |\n| `seed` | `None` | Random seed for reproducibility |\n| `anticipation` | `0` | Anticipation periods |\n\n**Reference:** Chen, X., Sant'Anna, P. H. C., & Xie, H. (2025). Efficient Difference-in-Differences and Event Study Estimators.\n\n*See also: [Choosing an Estimator](../choosing_estimator.rst) for guidance on when to use EDiD vs other estimators.*"
   }
  ],
  "metadata": {