Address PR #199 review feedback: scope PT-Post claims, fix citation

- P1: Scope PT-Post/CS equivalence to post-treatment ATT(g,t) across all
  doc files (rst, README, notebook) — pre-treatment diagnostics may differ
- P2: Remove inappropriate cluster guidance from Webb bootstrap description
- P3: Fix citation initials (Chen X., Xie H.) and add full paper title

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

Author: igerber

README.md

@@ -1090,7 +1090,7 @@ results = edid.fit(data, outcome='outcome', unit='unit',
                    aggregate='all')
 results.print_summary()
 
-# PT-Post mode (reduces to Callaway-Sant'Anna)
+# PT-Post mode (matches CS for post-treatment effects)
 edid_post = EfficientDiD(pt_assumption="post")
 results_post = edid_post.fit(data, outcome='outcome', unit='unit',
                               time='period', first_treat='first_treat')
@@ -1100,7 +1100,7 @@ results_post = edid_post.fit(data, outcome='outcome', unit='unit',
 
 ```python
 EfficientDiD(
-    pt_assumption='all',            # 'all' (overidentified) or 'post' (= CS)
+    pt_assumption='all',            # 'all' (overidentified) or 'post' (matches CS post-treatment ATT)
     alpha=0.05,                     # Significance level
     n_bootstrap=0,                  # Bootstrap iterations (0 = analytical only)
     bootstrap_weights='rademacher', # 'rademacher', 'mammen', or 'webb'

docs/api/efficient_did.rst

@@ -10,7 +10,7 @@ This module implements the efficiency-bound-attaining estimator that:
 2. **Optimally weights** across comparison groups and baselines via the
    inverse covariance matrix Ω*
 3. **Supports two PT assumptions**: PT-All (overidentified, tighter SEs) and
-   PT-Post (just-identified, equivalent to Callaway-Sant'Anna)
+   PT-Post (just-identified, matches CS for post-treatment effects)
 4. **Uses EIF-based inference** for analytical standard errors and multiplier
    bootstrap
 
@@ -26,8 +26,8 @@ This module implements the efficiency-bound-attaining estimator that:
 - You want tighter confidence intervals than Callaway-Sant'Anna
 - You need a formal efficiency benchmark for comparing estimators
 
-**Reference:** Chen, J., Sant'Anna, P. H. C., & Xie, Y. (2025). Efficient
-Difference-in-Differences. *Working Paper*.
+**Reference:** Chen, X., Sant'Anna, P. H. C., & Xie, H. (2025). Efficient
+Difference-in-Differences and Event Study Estimators. *Working Paper*.
 
 .. module:: diff_diff.efficient_did
 
@@ -94,7 +94,7 @@ Basic usage::
                        aggregate='all')
     results.print_summary()
 
-PT-Post mode (equivalent to Callaway-Sant'Anna)::
+PT-Post mode (matches CS for post-treatment ATT)::
 
     edid_post = EfficientDiD(pt_assumption="post")
     results_post = edid_post.fit(data, outcome='outcome', unit='unit',
@@ -145,6 +145,6 @@ Comparison with Other Staggered Estimators
      - Multiplier bootstrap
      - Multiplier bootstrap
    * - PT-Post equivalence
-     - Reduces to CS
+     - Matches CS post-treatment ATT(g,t)
      - Baseline
      - Different framework

docs/choosing_estimator.rst

@@ -65,7 +65,7 @@ Quick Reference
      - ATT with unit/time weights
    * - ``EfficientDiD``
      - Staggered adoption with optimal efficiency
-     - PT-All (overidentified) or PT-Post (= CS)
+     - PT-All (overidentified) or PT-Post
      - Group-time ATT(g,t), aggregations
    * - ``ContinuousDiD``
      - Continuous dose / treatment intensity

docs/tutorials/15_efficient_did.ipynb

@@ -155,51 +155,15 @@
    "cell_type": "markdown",
    "id": "e1ad14f5",
    "metadata": {},
-   "source": [
-    "## PT-All vs PT-Post\n",
-    "\n",
-    "EDiD supports two parallel trends assumptions:\n",
-    "\n",
-    "- **PT-All** (`pt_assumption=\"all\"`): Parallel trends holds across *all* pre-treatment periods. The model is overidentified --- more valid comparisons exist than needed --- and EDiD exploits this for tighter SEs.\n",
-    "- **PT-Post** (`pt_assumption=\"post\"`): Parallel trends holds only from `g-1` onward (the weaker, standard assumption). EDiD reduces to a single-baseline estimator, equivalent to Callaway-Sant'Anna.\n",
-    "\n",
-    "PT-All is the default because it delivers efficiency gains when the assumption holds. Use PT-Post if you're concerned about violations in early pre-treatment periods."
-   ]
+   "source": "## PT-All vs PT-Post\n\nEDiD supports two parallel trends assumptions:\n\n- **PT-All** (`pt_assumption=\"all\"`): Parallel trends holds across *all* pre-treatment periods. The model is overidentified --- more valid comparisons exist than needed --- and EDiD exploits this for tighter SEs.\n- **PT-Post** (`pt_assumption=\"post\"`): Parallel trends holds only from `g-1` onward (the weaker, standard assumption). EDiD uses a single baseline (`g-1`) per cohort, matching `CallawaySantAnna(control_group='never_treated')` for post-treatment ATT(g,t). Pre-treatment diagnostics may differ from CS's default `base_period='varying'`.\n\nPT-All is the default because it delivers efficiency gains when the assumption holds. Use PT-Post if you're concerned about violations in early pre-treatment periods."
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "id": "35f70199",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "# Fit under both assumptions\n",
-    "results_all = EfficientDiD(pt_assumption=\"all\").fit(\n",
-    "    data, outcome='outcome', unit='unit', time='period',\n",
-    "    first_treat='first_treat', aggregate='all')\n",
-    "\n",
-    "results_post = EfficientDiD(pt_assumption=\"post\").fit(\n",
-    "    data, outcome='outcome', unit='unit', time='period',\n",
-    "    first_treat='first_treat', aggregate='all')\n",
-    "\n",
-    "# Compare with Callaway-Sant'Anna\n",
-    "results_cs = CallawaySantAnna().fit(\n",
-    "    data, outcome='outcome', unit='unit', time='period',\n",
-    "    first_treat='first_treat')\n",
-    "\n",
-    "print(\"PT-All vs PT-Post vs Callaway-Sant'Anna\")\n",
-    "print(\"=\" * 65)\n",
-    "print(f\"{'Estimator':<25} {'ATT':>10} {'SE':>10} {'CI Width':>12}\")\n",
-    "print(\"-\" * 65)\n",
-    "for name, r in [(\"EDiD (PT-All)\", results_all),\n",
-    "                (\"EDiD (PT-Post)\", results_post),\n",
-    "                (\"CallawaySantAnna\", results_cs)]:\n",
-    "    ci_width = r.overall_conf_int[1] - r.overall_conf_int[0]\n",
-    "    print(f\"{name:<25} {r.overall_att:>10.4f} {r.overall_se:>10.4f} {ci_width:>12.4f}\")\n",
-    "print()\n",
-    "print(\"Notice: PT-Post and CS produce identical ATTs. PT-All has the same\")\n",
-    "print(\"ATT but tighter SEs --- this is the efficiency gain.\")"
-   ]
+   "source": "# Fit under both assumptions\nresults_all = EfficientDiD(pt_assumption=\"all\").fit(\n    data, outcome='outcome', unit='unit', time='period',\n    first_treat='first_treat', aggregate='all')\n\nresults_post = EfficientDiD(pt_assumption=\"post\").fit(\n    data, outcome='outcome', unit='unit', time='period',\n    first_treat='first_treat', aggregate='all')\n\n# Compare with Callaway-Sant'Anna\nresults_cs = CallawaySantAnna().fit(\n    data, outcome='outcome', unit='unit', time='period',\n    first_treat='first_treat')\n\nprint(\"PT-All vs PT-Post vs Callaway-Sant'Anna\")\nprint(\"=\" * 65)\nprint(f\"{'Estimator':<25} {'ATT':>10} {'SE':>10} {'CI Width':>12}\")\nprint(\"-\" * 65)\nfor name, r in [(\"EDiD (PT-All)\", results_all),\n                (\"EDiD (PT-Post)\", results_post),\n                (\"CallawaySantAnna\", results_cs)]:\n    ci_width = r.overall_conf_int[1] - r.overall_conf_int[0]\n    print(f\"{name:<25} {r.overall_att:>10.4f} {r.overall_se:>10.4f} {ci_width:>12.4f}\")\nprint()\nprint(\"PT-Post and CS produce identical post-treatment ATTs.\")"
   },
   {
    "cell_type": "markdown",
@@ -324,16 +288,7 @@
    "cell_type": "markdown",
    "id": "a89342da",
    "metadata": {},
-   "source": [
-    "## Bootstrap Inference\n",
-    "\n",
-    "EDiD supports multiplier bootstrap for inference. The bootstrap perturbs the influence function values with random weights to obtain bootstrap distributions of all parameters.\n",
-    "\n",
-    "Three weight distributions are available:\n",
-    "- **Rademacher** (default): $\\pm 1$ with equal probability --- standard choice, works well in most settings\n",
-    "- **Mammen**: Two-point distribution that matches third moments\n",
-    "- **Webb**: Six-point distribution, recommended when clusters are very few (<10)"
-   ]
+   "source": "## Bootstrap Inference\n\nEDiD supports multiplier bootstrap for inference. The bootstrap perturbs the influence function values with random weights to obtain bootstrap distributions of all parameters.\n\nThree weight distributions are available:\n- **Rademacher** (default): $\\pm 1$ with equal probability --- standard choice, works well in most settings\n- **Mammen**: Two-point distribution that matches third moments\n- **Webb**: Six-point distribution with wider support"
   },
   {
    "cell_type": "code",
@@ -544,37 +499,7 @@
    "cell_type": "markdown",
    "id": "ef99ee47",
    "metadata": {},
-   "source": [
-    "## Summary\n",
-    "\n",
-    "**Key takeaways:**\n",
-    "\n",
-    "1. EDiD achieves the **semiparametric efficiency bound** for ATT estimation in staggered designs\n",
-    "2. Under **PT-All**, EDiD exploits overidentification for tighter SEs than CS\n",
-    "3. Under **PT-Post**, EDiD reduces to **exactly Callaway-Sant'Anna**\n",
-    "4. The efficiency gain comes from optimally weighting across all valid (comparison group, baseline) pairs\n",
-    "5. **Event study** and **group** aggregations work just like CS\n",
-    "6. **Multiplier bootstrap** provides robust inference with Rademacher, Mammen, or Webb weights\n",
-    "7. **Condition numbers** flag potentially unstable weight matrices\n",
-    "8. **Anticipation** shifts the effective treatment boundary for pre-treatment effects\n",
-    "9. Phase 1 is **no-covariates only** --- Phase 2 will add covariate support\n",
-    "10. 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, = CS) |\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, J., Sant'Anna, P. H. C., & Xie, Y. (2025). Efficient Difference-in-Differences. *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. *Working Paper*.\n\n*See also: [Choosing an Estimator](../choosing_estimator.rst) for guidance on when to use EDiD vs other estimators.*"
   }
  ],
  "metadata": {
@@ -584,4 +509,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}