Merge pull request #258 from igerber/impuation-tutorial-edits

Update ImputationDiD tutorial to show pretrends=True event study

Author: igerber

docs/tutorials/11_imputation_did.ipynb

@@ -69,29 +69,14 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": [
-    "## Event Study\n",
-    "\n",
-    "Event study aggregation estimates treatment effects at each relative time horizon, enabling visualization of dynamic treatment effects and pre-trend assessment."
-   ]
+   "source": "## Event Study with Pre-Trend Diagnostics\n\nEvent study aggregation estimates treatment effects at each relative time horizon. Setting `pretrends=True` adds **pre-period coefficients** (negative horizons) to the event study, enabling a diagnostic check of the parallel trends assumption.\n\nUnder parallel trends, pre-period coefficients should cluster around zero — indicating no differential trends before treatment. The reference period (h = -1) is normalized to zero by construction."
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "# Fit with event study aggregation\n",
-    "est = ImputationDiD()\n",
-    "results_es = est.fit(data, outcome='outcome', unit='unit', time='period',\n",
-    "                     first_treat='first_treat', aggregate='event_study')\n",
-    "\n",
-    "# Plot event study\n",
-    "if HAS_MATPLOTLIB:\n",
-    "    plot_event_study(results_es, title='Imputation DiD Event Study')\n",
-    "else:\n",
-    "    print(\"Install matplotlib to see visualizations: pip install matplotlib\")"
-   ]
+   "source": "# Fit with event study aggregation and pre-period coefficients\nest = ImputationDiD(pretrends=True)\nresults_es = est.fit(data, outcome='outcome', unit='unit', time='period',\n                     first_treat='first_treat', aggregate='event_study')\n\n# Plot event study — pre-period region is automatically shaded\nif HAS_MATPLOTLIB:\n    plot_event_study(results_es, title='Imputation DiD Event Study (with Pre-Trends)')\nelse:\n    print(\"Install matplotlib to see visualizations: pip install matplotlib\")"
   },
   {
    "cell_type": "code",
@@ -106,13 +91,7 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": [
-    "## Pre-Trend Test\n",
-    "\n",
-    "The imputation estimator includes a built-in pre-trend test (Equation 9 in the paper). It tests whether pre-treatment leads are jointly zero using a Wald F-test on untreated observations only.\n",
-    "\n",
-    "A key advantage: the pre-trend test is **independent** of the treatment effect estimator (Proposition 9), avoiding the pre-testing problem identified by Roth (2022)."
-   ]
+   "source": "## Formal Pre-Trend Test\n\nThe event study plot above gives a **visual** diagnostic — do pre-period coefficients look close to zero? For a **statistical** check, `pretrend_test()` runs a Wald F-test on whether all pre-treatment leads are jointly zero (Equation 9 in the paper). This complements the plot: the eye spots patterns, the F-test quantifies evidence consistent with parallel trends.\n\nNote: `pretrend_test()` does not require `pretrends=True` — it runs its own internal lead regression on untreated observations, independent of the treatment effect estimator (Proposition 9). This avoids the pre-testing problem identified by Roth (2022)."
   },
   {
    "cell_type": "code",
@@ -256,4 +235,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}