diff --git a/docs/tutorials/11_imputation_did.ipynb b/docs/tutorials/11_imputation_did.ipynb
index c953d463..f1499ae3 100644
--- a/docs/tutorials/11_imputation_did.ipynb
+++ b/docs/tutorials/11_imputation_did.ipynb
@@ -9,7 +9,7 @@
     "This tutorial demonstrates the `ImputationDiD` estimator, which implements the efficient imputation approach from Borusyak, Jaravel & Spiess (2024), \"Revisiting Event-Study Designs: Robust and Efficient Estimation\", *Review of Economic Studies*.\n",
     "\n",
     "**When to use ImputationDiD:**\n",
-    "- Staggered adoption settings where treatment effects may be **homogeneous** across cohorts and time \u2014 produces ~50% shorter CIs than Callaway-Sant'Anna\n",
+    "- Staggered adoption settings where treatment effects may be **homogeneous** across cohorts and time — produces ~50% shorter CIs than Callaway-Sant'Anna\n",
     "- When you want to use **all untreated observations** (never-treated + not-yet-treated) for maximum efficiency\n",
     "- As a complement to Callaway-Sant'Anna or Sun-Abraham: if all three agree, results are robust; if they disagree, investigate heterogeneity"
    ]
@@ -27,7 +27,16 @@
     "from diff_diff import (\n",
     "    ImputationDiD, CallawaySantAnna, SunAbraham,\n",
     "    generate_staggered_data, plot_event_study\n",
-    ")"
+    ")\n",
+    "\n",
+    "# For nicer plots (optional)\n",
+    "try:\n",
+    "    import matplotlib.pyplot as plt\n",
+    "    plt.style.use('seaborn-v0_8-whitegrid')\n",
+    "    HAS_MATPLOTLIB = True\n",
+    "except ImportError:\n",
+    "    HAS_MATPLOTLIB = False\n",
+    "    print(\"matplotlib not installed - visualization examples will be skipped\")"
    ]
   },
   {
@@ -78,7 +87,10 @@
     "                     first_treat='first_treat', aggregate='event_study')\n",
     "\n",
     "# Plot event study\n",
-    "plot_event_study(results_es, title='Imputation DiD Event Study')"
+    "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\")"
    ]
   },
   {
@@ -122,7 +134,7 @@
    "source": [
     "## Comparison with Other Estimators\n",
     "\n",
-    "Under homogeneous treatment effects, ImputationDiD, Callaway-Sant'Anna, and Sun-Abraham should produce similar point estimates. The key difference is efficiency \u2014 ImputationDiD produces shorter confidence intervals."
+    "Under homogeneous treatment effects, ImputationDiD, Callaway-Sant'Anna, and Sun-Abraham should produce similar point estimates. The key difference is efficiency — ImputationDiD produces shorter confidence intervals."
    ]
   },
   {
diff --git a/docs/tutorials/12_two_stage_did.ipynb b/docs/tutorials/12_two_stage_did.ipynb
index 87c9e609..4ff1ab60 100644
--- a/docs/tutorials/12_two_stage_did.ipynb
+++ b/docs/tutorials/12_two_stage_did.ipynb
@@ -27,7 +27,16 @@
     "from diff_diff import (\n",
     "    TwoStageDiD, ImputationDiD, CallawaySantAnna,\n",
     "    generate_staggered_data, plot_event_study\n",
-    ")"
+    ")\n",
+    "\n",
+    "# For nicer plots (optional)\n",
+    "try:\n",
+    "    import matplotlib.pyplot as plt\n",
+    "    plt.style.use('seaborn-v0_8-whitegrid')\n",
+    "    HAS_MATPLOTLIB = True\n",
+    "except ImportError:\n",
+    "    HAS_MATPLOTLIB = False\n",
+    "    print(\"matplotlib not installed - visualization examples will be skipped\")"
    ]
   },
   {
@@ -80,7 +89,10 @@
     "                     first_treat='first_treat', aggregate='event_study')\n",
     "\n",
     "# Plot event study\n",
-    "plot_event_study(results_es, title='Two-Stage DiD Event Study')"
+    "if HAS_MATPLOTLIB:\n",
+    "    plot_event_study(results_es, title='Two-Stage DiD Event Study')\n",
+    "else:\n",
+    "    print(\"Install matplotlib to see visualizations: pip install matplotlib\")"
    ]
   },
   {
@@ -96,7 +108,17 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "## Per-Observation Treatment Effects\n\nBoth `TwoStageDiD` and `ImputationDiD` provide a `treatment_effects` DataFrame containing one row per treated observation with:\n- `tau_hat`: the residualized outcome (actual outcome minus estimated counterfactual)\n- The unit and time columns (using the original column names from the input data, e.g., `unit` and `period`)\n- `rel_time`: relative time since treatment\n- `weight`: aggregation weight — `1/n_valid` for observations with finite `tau_hat`, `0` for NaN rows (e.g., rank-deficient cases)\n\nThis enables granular analysis: examining which units or periods drive the aggregate effect, detecting outliers, or constructing custom aggregation schemes."
+   "source": [
+    "## Per-Observation Treatment Effects\n",
+    "\n",
+    "Both `TwoStageDiD` and `ImputationDiD` provide a `treatment_effects` DataFrame containing one row per treated observation with:\n",
+    "- `tau_hat`: the residualized outcome (actual outcome minus estimated counterfactual)\n",
+    "- The unit and time columns (using the original column names from the input data, e.g., `unit` and `period`)\n",
+    "- `rel_time`: relative time since treatment\n",
+    "- `weight`: aggregation weight — `1/n_valid` for observations with finite `tau_hat`, `0` for NaN rows (e.g., rank-deficient cases)\n",
+    "\n",
+    "This enables granular analysis: examining which units or periods drive the aggregate effect, detecting outliers, or constructing custom aggregation schemes."
+   ]
   },
   {
    "cell_type": "code",
@@ -115,7 +137,15 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "## Comparison with Other Estimators\n\nTwoStageDiD and ImputationDiD produce **identical point estimates** because both estimate fixed effects on untreated observations and use them to residualize outcomes. The key difference is the variance estimator: TwoStageDiD uses the GMM sandwich from Butts & Gardner (2022), while ImputationDiD uses the conservative variance from Borusyak et al. (2024, Theorem 3).\n\nCallawaySantAnna uses a fundamentally different estimation approach — computing group-time ATT(g,t) effects via outcome regression, IPW, or doubly robust methods, then aggregating — so point estimates may differ, especially under heterogeneous effects. It uses analytical influence-function standard errors by default, with optional multiplier bootstrap when `n_bootstrap > 0`.\n\n*Note: Tutorial 11 compared ImputationDiD against CallawaySantAnna and SunAbraham. Here we focus on the TwoStageDiD vs ImputationDiD point-estimate identity, with CallawaySantAnna as a widely used reference point. For SunAbraham comparisons, see Tutorial 11.*"
+   "source": [
+    "## Comparison with Other Estimators\n",
+    "\n",
+    "TwoStageDiD and ImputationDiD produce **identical point estimates** because both estimate fixed effects on untreated observations and use them to residualize outcomes. The key difference is the variance estimator: TwoStageDiD uses the GMM sandwich from Butts & Gardner (2022), while ImputationDiD uses the conservative variance from Borusyak et al. (2024, Theorem 3).\n",
+    "\n",
+    "CallawaySantAnna uses a fundamentally different estimation approach — computing group-time ATT(g,t) effects via outcome regression, IPW, or doubly robust methods, then aggregating — so point estimates may differ, especially under heterogeneous effects. It uses analytical influence-function standard errors by default, with optional multiplier bootstrap when `n_bootstrap > 0`.\n",
+    "\n",
+    "*Note: Tutorial 11 compared ImputationDiD against CallawaySantAnna and SunAbraham. Here we focus on the TwoStageDiD vs ImputationDiD point-estimate identity, with CallawaySantAnna as a widely used reference point. For SunAbraham comparisons, see Tutorial 11.*"
+   ]
   },
   {
    "cell_type": "code",
@@ -237,7 +267,22 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "## Summary\n\n| Feature | TwoStageDiD | ImputationDiD | CallawaySantAnna |\n|---------|-------------|---------------|------------------|\n| **Approach** | Residualize via FE, regress on treatment | Impute Y(0) via FE model | Group-time ATT(g,t) |\n| **Point estimates** | Identical to ImputationDiD | Identical to TwoStageDiD | Different weighting |\n| **Variance** | GMM sandwich (influence function) | Conservative (Theorem 3) | Analytical influence function (optional bootstrap) |\n| **Per-obs effects** | Yes (`treatment_effects`) | Yes (`treatment_effects`) | No |\n| **Pre-trend test** | Via event study pre-periods | Yes (built-in F-test) | Via event study pre-periods |\n| **Best for** | Robustness check, granular effects | Maximum efficiency under homogeneity | Heterogeneous effects |\n\n**References:**\n- Gardner, J. (2022). Two-stage differences in differences. *arXiv:2207.05943*.\n- Butts, K. & Gardner, J. (2022). did2s: Two-Stage Difference-in-Differences. *R Journal*, 14(1), 162-173."
+   "source": [
+    "## Summary\n",
+    "\n",
+    "| Feature | TwoStageDiD | ImputationDiD | CallawaySantAnna |\n",
+    "|---------|-------------|---------------|------------------|\n",
+    "| **Approach** | Residualize via FE, regress on treatment | Impute Y(0) via FE model | Group-time ATT(g,t) |\n",
+    "| **Point estimates** | Identical to ImputationDiD | Identical to TwoStageDiD | Different weighting |\n",
+    "| **Variance** | GMM sandwich (influence function) | Conservative (Theorem 3) | Analytical influence function (optional bootstrap) |\n",
+    "| **Per-obs effects** | Yes (`treatment_effects`) | Yes (`treatment_effects`) | No |\n",
+    "| **Pre-trend test** | Via event study pre-periods | Yes (built-in F-test) | Via event study pre-periods |\n",
+    "| **Best for** | Robustness check, granular effects | Maximum efficiency under homogeneity | Heterogeneous effects |\n",
+    "\n",
+    "**References:**\n",
+    "- Gardner, J. (2022). Two-stage differences in differences. *arXiv:2207.05943*.\n",
+    "- Butts, K. & Gardner, J. (2022). did2s: Two-Stage Difference-in-Differences. *R Journal*, 14(1), 162-173."
+   ]
   }
  ],
  "metadata": {
@@ -247,4 +292,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
\ No newline at end of file
+}