Explain CS vs SA pre-period discrepancy in Tutorial 02

docs/methodology/REGISTRY.md

@@ -229,6 +229,11 @@ Aggregations:
   - "universal": All comparisons use g-anticipation-1 as base
   - Both produce identical post-treatment ATT(g,t); differ only pre-treatment
   - Matches R `did::att_gt()` base_period parameter
+- Base period interaction with Sun-Abraham comparison:
+  - CS with `base_period="varying"` produces different pre-treatment estimates than SA
+  - This is expected: CS uses consecutive comparisons, SA uses fixed reference (e=-1-anticipation)
+  - Use `base_period="universal"` for methodologically comparable pre-treatment effects
+  - Post-treatment effects match regardless of base_period setting
 - Control group with `control_group="not_yet_treated"`:
   - Always excludes cohort g from controls when computing ATT(g,t)
   - This applies to both pre-treatment (t < g) and post-treatment (t >= g) periods
@@ -257,7 +262,7 @@ Aggregations:
 *Assumption checks / warnings:*
 - Requires never-treated units as control group
 - Warns if treatment effects may be heterogeneous across cohorts (which the method handles)
-- Reference period must be specified (default: e=-1)
+- Reference period: e=-1-anticipation (defaults to e=-1 when anticipation=0)
 
 *Estimator equation (as implemented):*
 

docs/tutorials/02_staggered_did.ipynb

@@ -3,31 +3,7 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": [
-    "# Staggered Difference-in-Differences\n",
-    "\n",
-    "This notebook demonstrates how to handle **staggered treatment adoption** using modern DiD estimators. In staggered DiD settings:\n",
-    "\n",
-    "- Different units get treated at different times\n",
-    "- Traditional TWFE can give biased estimates due to \"forbidden comparisons\"\n",
-    "- Modern estimators compute group-time specific effects and aggregate them properly\n",
-    "\n",
-    "We'll cover:\n",
-    "1. Understanding staggered adoption\n",
-    "2. The problem with TWFE (and Goodman-Bacon decomposition)\n",
-    "3. The Callaway-Sant'Anna estimator\n",
-    "4. Group-time effects ATT(g,t)\n",
-    "5. Aggregating effects (simple, group, event-study)\n",
-    "6. Bootstrap inference for valid standard errors\n",
-    "7. Visualization\n",
-    "8. **Pre-treatment effects and parallel trends testing**\n",
-    "9. Different control group options\n",
-    "10. Handling anticipation effects\n",
-    "11. Adding covariates\n",
-    "12. Comparing with MultiPeriodDiD\n",
-    "13. Sun-Abraham interaction-weighted estimator\n",
-    "14. Comparing CS and SA as a robustness check"
-   ]
+   "source": "# Staggered Difference-in-Differences\n\nThis notebook demonstrates how to handle **staggered treatment adoption** using modern DiD estimators. In staggered DiD settings:\n\n- Different units get treated at different times\n- Traditional TWFE can give biased estimates due to \"forbidden comparisons\"\n- Modern estimators compute group-time specific effects and aggregate them properly\n\nWe'll cover:\n1. Understanding staggered adoption\n2. The problem with TWFE (and Goodman-Bacon decomposition)\n3. The Callaway-Sant'Anna estimator\n4. Group-time effects ATT(g,t)\n5. Aggregating effects (simple, group, event-study)\n6. Bootstrap inference for valid standard errors\n7. Visualization\n8. Pre-treatment effects and parallel trends testing\n9. Different control group options\n10. Handling anticipation effects\n11. Adding covariates\n12. Comparing with MultiPeriodDiD\n13. Sun-Abraham interaction-weighted estimator\n14. Comparing CS and SA as a robustness check"
   },
   {
    "cell_type": "code",
@@ -834,85 +810,19 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": [
-    "## 14. Comparing CS and SA as a Robustness Check\n",
-    "\n",
-    "Running both estimators provides a useful robustness check. When they agree, results are more credible."
-   ]
+   "source": "## 14. Comparing CS and SA as a Robustness Check\n\nRunning both estimators provides a useful robustness check. When they agree, results are more credible.\n\n### Understanding Pre-Period Differences\n\nYou may notice that **post-treatment effects align closely** between CS and SA, but **pre-treatment effects can differ in magnitude and significance**. This is expected methodological behavior, not a bug.\n\n**Why the difference?**\n\n1. **Callaway-Sant'Anna with `base_period=\"varying\"` (default)**:\n   - Pre-treatment effects use **consecutive period comparisons** (period t vs period t-1)\n   - Each pre-period coefficient represents a one-period change\n   - These smaller incremental changes often yield lower t-statistics\n\n2. **Sun-Abraham**:\n   - Uses a **fixed reference period** (e=-1 when anticipation=0, or e=-1-anticipation otherwise)\n   - All coefficients are deviations from this single reference\n   - Pre-period coefficients show cumulative difference from the reference\n\n**To make CS pre-periods more comparable to SA**, use `base_period=\"universal\"`:\n\n```python\ncs_universal = CallawaySantAnna(base_period=\"universal\")\n```\n\nThis makes CS compare all periods to g-1 (like SA), producing more similar pre-treatment estimates."
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "# Compare overall ATT from both estimators\n",
-    "print(\"Robustness Check: CS vs SA\")\n",
-    "print(\"=\" * 50)\n",
-    "print(f\"{'Estimator':<25} {'Overall ATT':>12} {'SE':>10}\")\n",
-    "print(\"-\" * 50)\n",
-    "print(f\"{'Callaway-SantAnna':<25} {results_cs.overall_att:>12.4f} {results_cs.overall_se:>10.4f}\")\n",
-    "print(f\"{'Sun-Abraham':<25} {results_sa.overall_att:>12.4f} {results_sa.overall_se:>10.4f}\")\n",
-    "\n",
-    "# Compare event study effects\n",
-    "print(\"\\n\\nEvent Study Comparison:\")\n",
-    "print(f\"{'Rel. Time':>12} {'CS ATT':>10} {'SA ATT':>10} {'Difference':>12}\")\n",
-    "print(\"-\" * 50)\n",
-    "\n",
-    "# Use the pre-computed event_study_effects from results_cs\n",
-    "for rel_time in sorted(results_sa.event_study_effects.keys()):\n",
-    "    sa_eff = results_sa.event_study_effects[rel_time]['effect']\n",
-    "    if results_cs.event_study_effects and rel_time in results_cs.event_study_effects:\n",
-    "        cs_eff = results_cs.event_study_effects[rel_time]['effect']\n",
-    "        diff = sa_eff - cs_eff\n",
-    "        print(f\"{rel_time:>12} {cs_eff:>10.4f} {sa_eff:>10.4f} {diff:>12.4f}\")\n",
-    "\n",
-    "print(\"\\nSimilar results indicate robust findings across estimation methods\")"
-   ]
+   "source": "# Compare overall ATT from both estimators\nprint(\"Robustness Check: CS vs SA\")\nprint(\"=\" * 60)\nprint(f\"{'Estimator':<30} {'Overall ATT':>12} {'SE':>10}\")\nprint(\"-\" * 60)\nprint(f\"{'Callaway-Sant\\\\'Anna (varying)':<30} {results_cs.overall_att:>12.4f} {results_cs.overall_se:>10.4f}\")\nprint(f\"{'Sun-Abraham':<30} {results_sa.overall_att:>12.4f} {results_sa.overall_se:>10.4f}\")\n\n# Also fit CS with universal base period for comparison\ncs_universal = CallawaySantAnna(control_group=\"never_treated\", base_period=\"universal\")\nresults_cs_univ = cs_universal.fit(\n    df, outcome=\"outcome\", unit=\"unit\",\n    time=\"period\", first_treat=\"first_treat\",\n    aggregate=\"event_study\"\n)\n\n# Compare event study effects\nprint(\"\\n\\nEvent Study Comparison:\")\nprint(\"Note: Pre-periods differ due to base period methodology (see explanation above)\")\nprint(f\"{'Rel. Time':>10} {'CS (vary)':>12} {'CS (univ)':>12} {'SA':>10} {'Note':>20}\")\nprint(\"-\" * 70)\n\nfor rel_time in sorted(results_sa.event_study_effects.keys()):\n    sa_eff = results_sa.event_study_effects[rel_time]['effect']\n    cs_vary = results_cs.event_study_effects.get(rel_time, {}).get('effect', np.nan)\n    cs_univ = results_cs_univ.event_study_effects.get(rel_time, {}).get('effect', np.nan)\n    \n    note = \"pre (differs)\" if rel_time < 0 else \"post (matches)\"\n    print(f\"{rel_time:>10} {cs_vary:>12.4f} {cs_univ:>12.4f} {sa_eff:>10.4f} {note:>20}\")\n\nprint(\"\\nPost-treatment effects should be similar across all methods\")\nprint(\"Pre-treatment differences are expected due to base period methodology\")"
   },
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": [
-    "## Summary\n",
-    "\n",
-    "Key takeaways:\n",
-    "\n",
-    "1. **TWFE can be biased** with staggered adoption and heterogeneous effects\n",
-    "2. **Goodman-Bacon decomposition** reveals *why* TWFE fails by showing:\n",
-    "   - The implicit 2x2 comparisons and their weights\n",
-    "   - How much weight falls on \"forbidden comparisons\" (already-treated as controls)\n",
-    "3. **Callaway-Sant'Anna** properly handles staggered adoption by:\n",
-    "   - Computing group-time specific effects ATT(g,t)\n",
-    "   - Only using valid comparison groups\n",
-    "   - Properly aggregating effects\n",
-    "4. **Sun-Abraham** provides an alternative approach using:\n",
-    "   - Interaction-weighted regression with cohort x relative-time indicators\n",
-    "   - Different weighting scheme than CS\n",
-    "   - More efficient under homogeneous effects\n",
-    "5. **Run both CS and SA** as a robustness check—when they agree, results are more credible\n",
-    "6. **Aggregation options**:\n",
-    "   - `\"simple\"`: Overall ATT\n",
-    "   - `\"group\"`: ATT by cohort\n",
-    "   - `\"event\"`: ATT by event time (for event-study plots)\n",
-    "7. **Bootstrap inference** provides valid standard errors and confidence intervals:\n",
-    "   - Use `n_bootstrap` parameter to enable multiplier bootstrap\n",
-    "   - Choose weight type: `'rademacher'`, `'mammen'`, or `'webb'`\n",
-    "   - Bootstrap results include SEs, CIs, and p-values for all aggregations\n",
-    "8. **Pre-treatment effects** provide parallel trends diagnostics:\n",
-    "   - Use `base_period=\"varying\"` for consecutive period comparisons\n",
-    "   - Pre-treatment ATT(g,t) should be near zero\n",
-    "   - 95% CIs including zero is consistent with parallel trends\n",
-    "   - See Tutorial 07 for pre-trends power analysis (Roth 2022)\n",
-    "9. **Control group choices** affect efficiency and assumptions:\n",
-    "   - `\"never_treated\"`: Stronger parallel trends assumption\n",
-    "   - `\"not_yet_treated\"`: Weaker assumption, uses more data\n",
-    "\n",
-    "For more details, see:\n",
-    "- Callaway, B., & Sant'Anna, P. H. (2021). Difference-in-differences with multiple time periods. *Journal of Econometrics*.\n",
-    "- Sun, L., & Abraham, S. (2021). Estimating dynamic treatment effects in event studies with heterogeneous treatment effects. *Journal of Econometrics*.\n",
-    "- Goodman-Bacon, A. (2021). Difference-in-differences with variation in treatment timing. *Journal of Econometrics*."
-   ]
+   "source": "## Summary\n\nKey takeaways:\n\n1. **TWFE can be biased** with staggered adoption and heterogeneous effects\n2. **Goodman-Bacon decomposition** reveals *why* TWFE fails by showing:\n   - The implicit 2x2 comparisons and their weights\n   - How much weight falls on \"forbidden comparisons\" (already-treated as controls)\n3. **Callaway-Sant'Anna** properly handles staggered adoption by:\n   - Computing group-time specific effects ATT(g,t)\n   - Only using valid comparison groups\n   - Properly aggregating effects\n4. **Sun-Abraham** provides an alternative approach using:\n   - Interaction-weighted regression with cohort x relative-time indicators\n   - Different weighting scheme than CS\n   - More efficient under homogeneous effects\n5. **Run both CS and SA** as a robustness check—when they agree, results are more credible\n6. **Aggregation options**:\n   - `\"simple\"`: Overall ATT\n   - `\"group\"`: ATT by cohort\n   - `\"event\"`: ATT by event time (for event-study plots)\n7. **Bootstrap inference** provides valid standard errors and confidence intervals:\n   - Use `n_bootstrap` parameter to enable multiplier bootstrap\n   - Choose weight type: `'rademacher'`, `'mammen'`, or `'webb'`\n   - Bootstrap results include SEs, CIs, and p-values for all aggregations\n8. **Pre-treatment effects** provide parallel trends diagnostics:\n   - Use `base_period=\"varying\"` for consecutive period comparisons\n   - Pre-treatment ATT(g,t) should be near zero\n   - 95% CIs including zero is consistent with parallel trends\n   - See Tutorial 07 for pre-trends power analysis (Roth 2022)\n9. **Control group choices** affect efficiency and assumptions:\n   - `\"never_treated\"`: Stronger parallel trends assumption\n   - `\"not_yet_treated\"`: Weaker assumption, uses more data\n10. **CS vs SA pre-period differences are expected**:\n    - Post-treatment effects should be similar (robustness check)\n    - Pre-treatment effects differ due to base period methodology\n    - CS (varying): consecutive comparisons → one-period changes\n    - SA: fixed reference (e=-1-anticipation) → cumulative deviations\n    - Use `base_period=\"universal\"` in CS for comparable pre-periods\n\nFor more details, see:\n- Callaway, B., & Sant'Anna, P. H. (2021). Difference-in-differences with multiple time periods. *Journal of Econometrics*.\n- Sun, L., & Abraham, S. (2021). Estimating dynamic treatment effects in event studies with heterogeneous treatment effects. *Journal of Econometrics*.\n- Goodman-Bacon, A. (2021). Difference-in-differences with variation in treatment timing. *Journal of Econometrics*."
   }
  ],
  "metadata": {
@@ -922,4 +832,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}

tests/test_sun_abraham.py

@@ -526,6 +526,107 @@ def test_both_recover_treatment_effect(self):
         assert abs(sa_results.overall_att - 3.0) < 2.0
         assert abs(cs_results.overall_att - 3.0) < 2.0
 
+    def test_pre_period_difference_expected_between_cs_sa(self):
+        """Pre-periods differ between CS (varying) and SA; post-periods match.
+
+        This is expected: CS uses consecutive comparisons, SA uses fixed reference.
+        CS with base_period="universal" should be closer to SA for pre-periods.
+        """
+        from diff_diff import CallawaySantAnna
+
+        data = generate_staggered_data(
+            n_units=200, treatment_effect=3.0, seed=42
+        )
+
+        # Sun-Abraham (uses fixed reference period e=-1)
+        sa = SunAbraham()
+        sa_results = sa.fit(
+            data,
+            outcome="outcome",
+            unit="unit",
+            time="time",
+            first_treat="first_treat",
+        )
+
+        # Callaway-Sant'Anna with varying base (default: consecutive comparisons)
+        cs_varying = CallawaySantAnna(base_period="varying")
+        cs_varying_results = cs_varying.fit(
+            data,
+            outcome="outcome",
+            unit="unit",
+            time="time",
+            first_treat="first_treat",
+            aggregate="event_study",
+        )
+
+        # Callaway-Sant'Anna with universal base (all compare to g-1)
+        cs_universal = CallawaySantAnna(base_period="universal")
+        cs_universal_results = cs_universal.fit(
+            data,
+            outcome="outcome",
+            unit="unit",
+            time="time",
+            first_treat="first_treat",
+            aggregate="event_study",
+        )
+
+        # Find common event times
+        sa_times = set(sa_results.event_study_effects.keys())
+        cs_varying_times = set(cs_varying_results.event_study_effects.keys())
+        cs_universal_times = set(cs_universal_results.event_study_effects.keys())
+        common_times = sa_times & cs_varying_times & cs_universal_times
+
+        # Separate pre and post periods
+        pre_times = [t for t in common_times if t < 0]
+        post_times = [t for t in common_times if t > 0]
+
+        # Post-treatment effects should match across all methods
+        for t in post_times:
+            sa_eff = sa_results.event_study_effects[t]["effect"]
+            cs_vary_eff = cs_varying_results.event_study_effects[t]["effect"]
+            cs_univ_eff = cs_universal_results.event_study_effects[t]["effect"]
+
+            # All three should be similar for post-treatment
+            max_se = max(
+                sa_results.event_study_effects[t]["se"],
+                cs_varying_results.event_study_effects[t]["se"],
+                cs_universal_results.event_study_effects[t]["se"],
+            )
+            assert abs(sa_eff - cs_vary_eff) < 3 * max_se, (
+                f"Post-period t={t}: SA and CS(varying) differ too much: "
+                f"SA={sa_eff:.4f}, CS(vary)={cs_vary_eff:.4f}"
+            )
+            assert abs(sa_eff - cs_univ_eff) < 3 * max_se, (
+                f"Post-period t={t}: SA and CS(universal) differ too much: "
+                f"SA={sa_eff:.4f}, CS(univ)={cs_univ_eff:.4f}"
+            )
+
+        # Require pre-periods exist for this test to be meaningful
+        assert len(pre_times) > 0, (
+            "Test requires pre-treatment periods to validate methodology difference. "
+            "Increase n_periods or adjust cohort timing in test data."
+        )
+
+        # Compute total absolute differences
+        total_diff_varying = 0.0
+        total_diff_universal = 0.0
+        for t in pre_times:
+            sa_eff = sa_results.event_study_effects[t]["effect"]
+            cs_vary_eff = cs_varying_results.event_study_effects[t]["effect"]
+            cs_univ_eff = cs_universal_results.event_study_effects[t]["effect"]
+
+            total_diff_varying += abs(sa_eff - cs_vary_eff)
+            total_diff_universal += abs(sa_eff - cs_univ_eff)
+
+        # CS(universal) should generally be closer to SA than CS(varying)
+        # for pre-treatment periods (due to similar reference period approach)
+        # Allow some tolerance since weighting schemes still differ
+        assert total_diff_universal <= total_diff_varying + 0.5, (
+            f"Expected CS(universal) to be closer to SA than CS(varying) for pre-periods. "
+            f"Got: CS(univ)-SA diff={total_diff_universal:.4f}, "
+            f"CS(vary)-SA diff={total_diff_varying:.4f}"
+        )
+
     def test_agreement_under_homogeneous_effects(self):
         """Test that SA and CS agree under homogeneous treatment effects."""
         from diff_diff import CallawaySantAnna