diff --git a/CHANGELOG.md b/CHANGELOG.md
index 7e4938c66..19d3e2a13 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -39,6 +39,15 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
   framing - that layer shipped in 3.9 and this is its consumer. The
   estimator-owned-totals remainder for CS/EfficientDiD/StackedDiD stays
   tracked in DEFERRED.md.
+- **Tutorial 28: The Scholarship Illusion** (`docs/tutorials/28_rdd_scholarship_illusion.ipynb`) -
+  the regression discontinuity walkthrough: a naive above-vs-below comparison
+  overstates a merit scholarship's effect fivefold; `RDPlot` makes the confound
+  and the cutoff jump visible, sharp `RegressionDiscontinuity` recovers the
+  offer's intent-to-treat effect with robust bias-corrected inference, and the
+  tutorial closes with the validity toolkit (estimator-as-balance-test, placebo
+  cutoffs, bandwidth sensitivity), fuzzy RD via `takeup=` (first stage +
+  complier LATE), and CCFT 2019 covariate adjustment (precision, not
+  identification).
 
 ### Changed
 - **Narrative docs migrated off the deprecated fit-time `aggregate=`** (the
diff --git a/docs/doc-deps.yaml b/docs/doc-deps.yaml
index 7ae7cc00a..059aa15d8 100644
--- a/docs/doc-deps.yaml
+++ b/docs/doc-deps.yaml
@@ -504,6 +504,8 @@ sources:
         type: user_guide
       - path: docs/migration-4.0.md
         type: user_guide
+      - path: docs/tutorials/28_rdd_scholarship_illusion.ipynb
+        type: tutorial
   diff_diff/rdplot.py:
     drift_risk: medium
     docs:
@@ -526,6 +528,8 @@ sources:
         type: user_guide
       - path: docs/migration-4.0.md
         type: user_guide
+      - path: docs/tutorials/28_rdd_scholarship_illusion.ipynb
+        type: tutorial
   diff_diff/_rdrobust_port.py:
     drift_risk: high
     docs:
diff --git a/docs/tutorials/28_rdd_scholarship_illusion.ipynb b/docs/tutorials/28_rdd_scholarship_illusion.ipynb
new file mode 100644
index 000000000..b16710f3a
--- /dev/null
+++ b/docs/tutorials/28_rdd_scholarship_illusion.ipynb
@@ -0,0 +1,947 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# The Scholarship Illusion - Sharp and Fuzzy Regression Discontinuity\n",
+    "\n",
+    "A university awards a merit scholarship to every applicant who scores **65 or above**\n",
+    "on its entrance exam. Years later, the board wants to know whether the scholarship\n",
+    "actually raised graduates' earnings. The first analysis compares mean earnings above\n",
+    "and below the cutoff and finds a gap of almost **\\$15,000** - a number nobody should\n",
+    "believe, because high scorers out-earn low scorers *whether or not anyone hands them\n",
+    "money*. This tutorial uses **regression discontinuity (RDD)** to recover the credible\n",
+    "effect hiding inside that illusion: about \\$2,600 for receiving the *offer*, and about\n",
+    "\\$4,000 for the students the offer actually moves into the program.\n",
+    "\n",
+    "Along the way we cover the full RD workflow in `diff-diff`:\n",
+    "\n",
+    "- **`RDPlot`** - the Calonico-Cattaneo-Titiunik (2015) data-driven RD plot, the\n",
+    "  standard first look at any discontinuity design.\n",
+    "- **Sharp RD** with `RegressionDiscontinuity` - local-polynomial estimation with\n",
+    "  MSE-optimal bandwidths and robust bias-corrected inference (Calonico, Cattaneo &\n",
+    "  Titiunik 2014), matching R's `rdrobust` defaults.\n",
+    "- **The validity toolkit** - covariate balance, placebo cutoffs, and bandwidth\n",
+    "  sensitivity.\n",
+    "- **Fuzzy RD** - imperfect take-up, the first stage, and the complier LATE.\n",
+    "- **Covariate adjustment** (Calonico, Cattaneo, Farrell & Titiunik 2019) - and why\n",
+    "  covariates play a completely different role in RD than in DiD.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## When is RDD the right tool?\n",
+    "\n",
+    "This library is mostly about difference-in-differences, which needs treated and\n",
+    "comparison groups observed before and after treatment (as a panel or as repeated\n",
+    "cross-sections, depending on the estimator) plus a parallel-trends assumption. RDD\n",
+    "replaces that data demand with a design requirement: **treatment assigned by a known\n",
+    "rule** - a score crossing a threshold.\n",
+    "\n",
+    "- **You have pre/post data on treated and comparison groups** and believe some version\n",
+    "  of parallel trends - use the DiD estimators.\n",
+    "- **You have a single cross-section and an assignment rule** (an eligibility score, a\n",
+    "  vote share, an age or income threshold) - use `RegressionDiscontinuity`. If nobody\n",
+    "  can precisely manipulate their score, units just above and just below the cutoff\n",
+    "  should differ only in treatment. That comparability is the design's *identifying\n",
+    "  assumption* - made plausible by the rule, not guaranteed by it.\n",
+    "- **The price** is locality: RDD identifies the effect *at the cutoff*, not an average\n",
+    "  over the whole population. That is the estimand's honest scope, not a defect.\n",
+    "\n",
+    "Identification rests on **continuity**: absent treatment, average potential outcomes\n",
+    "are smooth functions of the running variable at the cutoff. That requires that nothing\n",
+    "*else* jumps at the threshold (no other program shares the cutoff) and that units do\n",
+    "not sort precisely around it. Continuity itself is untestable - what Act 4 checks are\n",
+    "its testable implications.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-15T18:31:46.832147Z",
+     "iopub.status.busy": "2026-08-15T18:31:46.831777Z",
+     "iopub.status.idle": "2026-08-15T18:31:47.544266Z",
+     "shell.execute_reply": "2026-08-15T18:31:47.543740Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "from diff_diff import RDPlot, RegressionDiscontinuity\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The data\n",
+    "\n",
+    "We simulate the admissions cohort so the truth is known by construction - every\n",
+    "estimate below can be checked against it. The ingredients:\n",
+    "\n",
+    "- **Running variable**: an exam `score`, roughly bell-shaped around 64, cutoff at 65.\n",
+    "  Just under half the cohort clears it.\n",
+    "- **Baseline earnings** rise *smoothly and nonlinearly* in the score (linear plus\n",
+    "  convex term) - this is the confound that makes the naive comparison a trap.\n",
+    "- **Predetermined covariates** (`parental_income`, `hs_gpa`) vary smoothly with the\n",
+    "  score and independently move earnings. Smooth-in-score means *balanced at the\n",
+    "  cutoff* - we will verify that rather than assume it.\n",
+    "- **Imperfect take-up**: crossing the cutoff triggers the *offer*, but only 72% of\n",
+    "  offered students enroll with the scholarship, and 6% of below-cutoff students\n",
+    "  enroll on equivalent outside aid. The treatment variable is therefore **funded\n",
+    "  enrollment** (scholarship-funded above the cutoff, outside-aid-funded below), and\n",
+    "  take-up is monotone in the offer by construction.\n",
+    "- **The truth**: funded enrollment raises earnings by exactly **\\$4,000**. The\n",
+    "  offer therefore raises *average* earnings at the cutoff by\n",
+    "  (0.72 - 0.06) x \\$4,000 = **\\$2,640** - the intent-to-treat effect.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-15T18:31:47.545513Z",
+     "iopub.status.busy": "2026-08-15T18:31:47.545409Z",
+     "iopub.status.idle": "2026-08-15T18:31:47.550043Z",
+     "shell.execute_reply": "2026-08-15T18:31:47.549649Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cohort: 8,000 students, 46.9% above the cutoff\n",
+      "take-up above cutoff: 73.6%\n",
+      "take-up below cutoff: 6.0%\n",
+      "true effect of enrolling: $4,000\n",
+      "true intent-to-treat effect of the offer: $2,640\n"
+     ]
+    }
+   ],
+   "source": [
+    "SEED = 25\n",
+    "N = 8_000\n",
+    "CUTOFF = 65.0\n",
+    "\n",
+    "TRUE_EFFECT = 4_000.0  # effect of funded enrollment, at the cutoff\n",
+    "P_ENROLL_IF_OFFERED = 0.72  # P(enroll | offered); complier share = 0.72 - 0.06\n",
+    "P_ALWAYS = 0.06  # below-cutoff students who find equivalent outside aid\n",
+    "\n",
+    "rng = np.random.default_rng(SEED)\n",
+    "\n",
+    "ability = rng.normal(0.0, 1.0, N)\n",
+    "score = np.clip(64 + 11 * ability + rng.normal(0, 4, N), 30, 100)\n",
+    "\n",
+    "# predetermined covariates: smooth in the score, hence balanced at the cutoff\n",
+    "parental_income = np.clip(\n",
+    "    45_000 + 600 * (score - 60) + rng.normal(0, 15_000, N), 8_000, None\n",
+    ")\n",
+    "hs_gpa = np.clip(2.9 + 0.016 * (score - 60) + rng.normal(0, 0.30, N), 0.0, 4.0)\n",
+    "\n",
+    "# the offer is deterministic in the score; enrollment is not\n",
+    "offer = (score >= CUTOFF).astype(int)\n",
+    "u = rng.uniform(size=N)\n",
+    "enrolled = np.where(offer == 1, (u < P_ENROLL_IF_OFFERED), (u < P_ALWAYS)).astype(int)\n",
+    "\n",
+    "# earnings at 28: smooth nonlinear baseline + covariates + the true effect\n",
+    "base = 24_000 + 320 * score + 7.0 * (score - 60) ** 2\n",
+    "earnings = (\n",
+    "    base\n",
+    "    + 0.35 * (parental_income - 45_000)\n",
+    "    + 4_000 * (hs_gpa - 2.9)\n",
+    "    + TRUE_EFFECT * enrolled\n",
+    "    + rng.normal(0, 4_500, N)\n",
+    ")\n",
+    "\n",
+    "df = pd.DataFrame(\n",
+    "    {\n",
+    "        \"score\": score,\n",
+    "        \"offer\": offer,\n",
+    "        \"enrolled\": enrolled,\n",
+    "        \"earnings\": earnings,\n",
+    "        \"parental_income\": parental_income,\n",
+    "        \"hs_gpa\": hs_gpa,\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "true_itt = (P_ENROLL_IF_OFFERED - P_ALWAYS) * TRUE_EFFECT\n",
+    "print(f\"cohort: {len(df):,} students, {df.offer.mean():.1%} above the cutoff\")\n",
+    "print(f\"take-up above cutoff: {df.loc[df.offer == 1, 'enrolled'].mean():.1%}\")\n",
+    "print(f\"take-up below cutoff: {df.loc[df.offer == 0, 'enrolled'].mean():.1%}\")\n",
+    "print(f\"true effect of enrolling: ${TRUE_EFFECT:,.0f}\")\n",
+    "print(f\"true intent-to-treat effect of the offer: ${true_itt:,.0f}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Act 1: the illusion\n",
+    "\n",
+    "The board's first analyst compares mean earnings on either side of the cutoff.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-15T18:31:47.563276Z",
+     "iopub.status.busy": "2026-08-15T18:31:47.563195Z",
+     "iopub.status.idle": "2026-08-15T18:31:47.641948Z",
+     "shell.execute_reply": "2026-08-15T18:31:47.641450Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x450 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "naive above-vs-below gap: $14,779\n",
+      "true offer effect at the cutoff: $2,640\n"
+     ]
+    }
+   ],
+   "source": [
+    "naive = (\n",
+    "    df.loc[df.offer == 1, \"earnings\"].mean()\n",
+    "    - df.loc[df.offer == 0, \"earnings\"].mean()\n",
+    ")\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(9, 4.5))\n",
+    "ax.scatter(df.score, df.earnings, s=4, alpha=0.12, color=\"tab:gray\")\n",
+    "for mask, color, lbl in (\n",
+    "    (df.offer == 0, \"tab:red\", \"below-cutoff mean\"),\n",
+    "    (df.offer == 1, \"tab:blue\", \"above-cutoff mean\"),\n",
+    "):\n",
+    "    m = df.loc[mask, \"earnings\"].mean()\n",
+    "    lo, hi = df.loc[mask, \"score\"].min(), df.loc[mask, \"score\"].max()\n",
+    "    ax.hlines(m, lo, hi, color=color, lw=2.5, label=f\"{lbl}: ${m:,.0f}\")\n",
+    "ax.axvline(CUTOFF, color=\"k\", ls=\"--\", lw=1)\n",
+    "ax.set_xlabel(\"exam score\")\n",
+    "ax.set_ylabel(\"earnings at 28 ($)\")\n",
+    "ax.set_title(f\"The naive comparison: a ${naive:,.0f} 'effect'\")\n",
+    "ax.legend(loc=\"upper left\")\n",
+    "plt.show()\n",
+    "\n",
+    "print(f\"naive above-vs-below gap: ${naive:,.0f}\")\n",
+    "print(f\"true offer effect at the cutoff: ${true_itt:,.0f}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**\\$14,779 - more than five times the true \\$2,640.** The picture shows why: earnings\n",
+    "climb steeply with the score everywhere, so the two group means mostly measure *who\n",
+    "scores high*, not *what the scholarship does*. Every student in the above-cutoff mean\n",
+    "outscored every student in the below-cutoff mean; the scholarship is a footnote to\n",
+    "that selection.\n",
+    "\n",
+    "The RD idea is to stop comparing distant students and compare students *at the\n",
+    "threshold* - a 64.9 scorer and a 65.1 scorer are essentially the same applicant, but\n",
+    "one gets the offer.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Act 2: look before you estimate - `RDPlot`\n",
+    "\n",
+    "The standard first move in any RD analysis is the CCT (2015) **RD plot**: bin the\n",
+    "running variable with a data-driven partition, plot per-bin outcome means, and overlay\n",
+    "separate global polynomial fits on each side. `RDPlot` matches R's `rdplot()`\n",
+    "numbers on its supported surface (documented edge-case deviations live in the\n",
+    "methodology registry), including the default mimicking-variance bin selector\n",
+    "(`binselect=\"esmv\"`),\n",
+    "whose bins deliberately preserve the raw data's vertical scatter instead of\n",
+    "oversmoothing it.\n",
+    "\n",
+    "Two views: the full support (where the *trend* is the star), and a window around the\n",
+    "cutoff (where the *jump* becomes visible). For the zoomed panel we drop the global\n",
+    "polynomial order from the default `p=4` to `p=2` - a ten-point window has no curvature\n",
+    "that needs a quartic, and the calmer fit makes the discontinuity easier to read.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-15T18:31:47.642930Z",
+     "iopub.status.busy": "2026-08-15T18:31:47.642862Z",
+     "iopub.status.idle": "2026-08-15T18:31:47.742581Z",
+     "shell.execute_reply": "2026-08-15T18:31:47.742155Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1100x420 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, (axa, axb) = plt.subplots(1, 2, figsize=(11, 4.2))\n",
+    "\n",
+    "full = RDPlot(cutoff=CUTOFF).fit(df, outcome=\"earnings\", running=\"score\")\n",
+    "full.plot(\n",
+    "    ax=axa,\n",
+    "    title=\"Full range: the trend dominates\",\n",
+    "    xlabel=\"exam score\",\n",
+    "    ylabel=\"earnings at 28 ($)\",\n",
+    ")\n",
+    "\n",
+    "window = df[df.score.between(CUTOFF - 10, CUTOFF + 10)]\n",
+    "zoom = RDPlot(cutoff=CUTOFF, p=2, ci=95).fit(\n",
+    "    window, outcome=\"earnings\", running=\"score\"\n",
+    ")\n",
+    "zoom.plot(\n",
+    "    ax=axb,\n",
+    "    title=\"±10 points around the cutoff: the jump appears\",\n",
+    "    xlabel=\"exam score\",\n",
+    "    ylabel=\"\",\n",
+    ")\n",
+    "fig.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The full-range panel is the naive analyst's mistake drawn honestly: a steep, convex\n",
+    "climb from about \\$34,000 to \\$80,000, of which the cutoff jump is a barely visible\n",
+    "step. The zoomed panel - with 95% per-bin confidence intervals via `ci=95` - shows a\n",
+    "clean vertical break of roughly \\$2,500-\\$3,000 at 65, with smooth behavior on\n",
+    "either side. That break is what the estimator will now measure precisely.\n",
+    "\n",
+    "`RDPlot` results also carry the full numeric surface (`summary()`, bin counts,\n",
+    "per-bin means and CIs in `vars_bins`) if you want the plot's ingredients rather than\n",
+    "its rendering.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Act 3: sharp RD - the effect of the offer\n",
+    "\n",
+    "`RegressionDiscontinuity` with default settings reproduces R's `rdrobust(y, x)`:\n",
+    "local-linear fits (`p=1`) with a triangular kernel on each side of the cutoff, a\n",
+    "common MSE-optimal bandwidth (`bwselect=\"mserd\"`), and **robust bias-corrected (RBC)\n",
+    "inference** - the CCT (2014) fix for the fact that a naively-chosen optimal bandwidth\n",
+    "leaves first-order smoothing bias in the estimate. Because the *offer* is a\n",
+    "deterministic function of the score, treating `offer` as the treatment makes this a\n",
+    "**sharp** design, and the estimand is the effect of crossing the cutoff - the\n",
+    "intent-to-treat effect of the offer.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-15T18:31:47.743593Z",
+     "iopub.status.busy": "2026-08-15T18:31:47.743527Z",
+     "iopub.status.idle": "2026-08-15T18:31:47.872472Z",
+     "shell.execute_reply": "2026-08-15T18:31:47.872020Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "========================================================================\n",
+      "            Sharp Regression Discontinuity (rdrobust parity)            \n",
+      "========================================================================\n",
+      "Cutoff:               65\n",
+      "Estimand:             sharp (ATE at the cutoff)\n",
+      "Kernel: triangular     Bandwidth selector: mserd\n",
+      "Order (p, q): (1, 2)       VCE: nn (nnmatch=3)   Masspoints: adjust\n",
+      "N = 8000 (4250 left / 3750 right); effective N_h = 2636/2486, N_b = 3547/3260\n",
+      "h = [10.6211, 10.6211]   b = [17.0699, 17.0699]\n",
+      "------------------------------------------------------------------------\n",
+      "Method                Coef.  Std. Err.        z    P>|z|[95% Conf. Int.]\n",
+      "------------------------------------------------------------------------\n",
+      "Conventional      2616.1764   438.1046    5.972    0.000  [1757.5071, 3474.8457]\n",
+      "Bias-Corrected    2628.7444   438.1046    6.000    0.000  [1770.0751, 3487.4137]\n",
+      "Robust            2628.7444   524.4613    5.012    0.000  [1600.8193, 3656.6696]\n",
+      "------------------------------------------------------------------------\n",
+      "Note: canonical att/se/t_stat/p_value/conf_int are the ROBUST row\n",
+      "(att = bias-corrected estimate; rdrobust prints the conventional\n",
+      "estimate as its headline coefficient - see att_conventional).\n",
+      "========================================================================\n"
+     ]
+    }
+   ],
+   "source": [
+    "sharp = RegressionDiscontinuity(cutoff=CUTOFF).fit(\n",
+    "    df, outcome=\"earnings\", running=\"score\"\n",
+    ")\n",
+    "print(sharp.summary())\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Reading the table:\n",
+    "\n",
+    "- **Three rows, one estimator.** *Conventional* is the raw local-linear jump with its\n",
+    "  conventional standard error; *Bias-Corrected* subtracts the estimated smoothing\n",
+    "  bias (using a quadratic fit within the wider bandwidth `b`); *Robust* keeps the\n",
+    "  bias-corrected point estimate but inflates the standard error to account for the\n",
+    "  noise in the bias estimate itself. Under CCT's regularity and bandwidth conditions\n",
+    "  the **Robust row's inference is asymptotically valid** and generally improves\n",
+    "  finite-sample coverage (nominal coverage is not guaranteed in every sample) - and\n",
+    "  `diff-diff` binds the canonical fields (`att`, `se`, `p_value`, `conf_int`) to it. (R's `rdrobust` prints the conventional estimate in\n",
+    "  its headline column while taking inference from the robust row; that conventional\n",
+    "  estimate is available here as `att_conventional`.)\n",
+    "- **The answer**: \\$2,629, 95% CI [\\$1,601, \\$3,657] - right on top of the \\$2,640\n",
+    "  truth, from a bandwidth of about 10.6 score points selected by the data\n",
+    "  (`h = 10.62`, effective n ≈ 5,100 of the 8,000 students).\n",
+    "- The naive comparison was off by a factor of five; the local comparison at the\n",
+    "  threshold nails it.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-15T18:31:47.873543Z",
+     "iopub.status.busy": "2026-08-15T18:31:47.873481Z",
+     "iopub.status.idle": "2026-08-15T18:31:47.875502Z",
+     "shell.execute_reply": "2026-08-15T18:31:47.875194Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "offer effect (robust bias-corrected): $2,629\n",
+      "95% CI: [$1,601, $3,657]\n",
+      "selected bandwidth h: 10.62 score points\n",
+      "true ITT: $2,640\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"offer effect (robust bias-corrected): ${sharp.att:,.0f}\")\n",
+    "print(f\"95% CI: [${sharp.conf_int[0]:,.0f}, ${sharp.conf_int[1]:,.0f}]\")\n",
+    "print(f\"selected bandwidth h: {sharp.h_left:.2f} score points\")\n",
+    "print(f\"true ITT: ${true_itt:,.0f}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Act 4: the validity toolkit\n",
+    "\n",
+    "An RD estimate is only as credible as its design checks. Three standard ones, all\n",
+    "runnable with the estimator itself.\n",
+    "\n",
+    "### 4a. Covariate balance\n",
+    "\n",
+    "Continuity has a testable implication: **predetermined covariates cannot jump at the\n",
+    "cutoff** - the scholarship offer cannot change your parents' income or your high-school\n",
+    "GPA. The balance test *is* an RD fit with the covariate as the outcome; a significant\n",
+    "jump would be evidence of sorting or manipulation.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-15T18:31:47.876439Z",
+     "iopub.status.busy": "2026-08-15T18:31:47.876370Z",
+     "iopub.status.idle": "2026-08-15T18:31:48.112641Z",
+     "shell.execute_reply": "2026-08-15T18:31:48.112251Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " parental_income: jump at cutoff =   20.154, p = 0.987\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "          hs_gpa: jump at cutoff =   -0.023, p = 0.273\n"
+     ]
+    }
+   ],
+   "source": [
+    "for cov in [\"parental_income\", \"hs_gpa\"]:\n",
+    "    bal = RegressionDiscontinuity(cutoff=CUTOFF).fit(\n",
+    "        df, outcome=cov, running=\"score\"\n",
+    "    )\n",
+    "    print(\n",
+    "        f\"{cov:>16}: jump at cutoff = {bal.att:>8.3f}, \"\n",
+    "        f\"p = {bal.p_value:.3f}\"\n",
+    "    )\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Parental income jumps by a statistically invisible \\$20 (p = 0.99) and GPA by -0.02\n",
+    "points (p = 0.27) - no evidence of a discontinuity in either. Read this as *failure\n",
+    "to reject* smoothness: a significant jump would have contradicted the design, and its\n",
+    "absence supports it, but no balance test can prove continuity of the (unobservable)\n",
+    "potential-outcome functions.\n",
+    "\n",
+    "### 4b. Placebo cutoffs\n",
+    "\n",
+    "At any threshold where nothing is assigned, the estimator should find nothing.\n",
+    "Practical guides examine one or more artificial cutoffs across the support;\n",
+    "for this illustration we use the median score on each side - two well-supported\n",
+    "artificial thresholds - fitting each only on its own side's data so the true\n",
+    "discontinuity cannot enter the placebo window.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-15T18:31:48.113749Z",
+     "iopub.status.busy": "2026-08-15T18:31:48.113685Z",
+     "iopub.status.idle": "2026-08-15T18:31:48.211931Z",
+     "shell.execute_reply": "2026-08-15T18:31:48.211570Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "placebo cutoff 56.9: jump = $  1,162, p = 0.333\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "placebo cutoff 72.6: jump = $   -349, p = 0.742\n"
+     ]
+    }
+   ],
+   "source": [
+    "left = df[df.score < CUTOFF]\n",
+    "right = df[df.score >= CUTOFF]\n",
+    "for side in (left, right):\n",
+    "    fake = float(side.score.median())\n",
+    "    placebo = RegressionDiscontinuity(cutoff=fake).fit(\n",
+    "        side, outcome=\"earnings\", running=\"score\"\n",
+    "    )\n",
+    "    print(\n",
+    "        f\"placebo cutoff {fake:.1f}: jump = ${placebo.att:>7,.0f}, \"\n",
+    "        f\"p = {placebo.p_value:.3f}\"\n",
+    "    )\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "No evidence of a jump at either placebo threshold (p = 0.33 and 0.74). Two placebo\n",
+    "cutoffs cannot rule out discontinuities everywhere else - but finding none where none\n",
+    "should exist is exactly the supporting evidence this check exists to collect.\n",
+    "\n",
+    "### 4c. Bandwidth sensitivity\n",
+    "\n",
+    "The bandwidth is the estimator's main tuning parameter, so referees will ask: does the\n",
+    "answer depend on it? Overriding the selector with fixed `h` values maps out the\n",
+    "sensitivity. One mechanical detail: passing `h` alone also sets the bias bandwidth\n",
+    "`b = h` (matching R), so each fit below is the complete RBC procedure run inside\n",
+    "that window - a joint sweep of both bandwidths, not a sweep of the local-linear\n",
+    "bandwidth with the bias correction held at the default fit's `b` (pass `b=` or\n",
+    "`rho=` explicitly for that design). Expect a mild bias-variance trade-off - tight\n",
+    "windows are noisier, wide windows lean harder on the polynomial approximation - but\n",
+    "the estimates should tell one consistent story.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-15T18:31:48.213036Z",
+     "iopub.status.busy": "2026-08-15T18:31:48.212958Z",
+     "iopub.status.idle": "2026-08-15T18:31:48.316722Z",
+     "shell.execute_reply": "2026-08-15T18:31:48.316380Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "h =  3: att = $ 3,905, 95% CI [$ 1,395, $ 6,415]\n",
+      "h =  5: att = $ 3,341, 95% CI [$ 1,441, $ 5,242]\n",
+      "h =  8: att = $ 3,005, 95% CI [$ 1,527, $ 4,484]\n",
+      "h = 12: att = $ 2,534, 95% CI [$ 1,336, $ 3,732]\n",
+      "h = 15: att = $ 2,607, 95% CI [$ 1,528, $ 3,685]\n"
+     ]
+    }
+   ],
+   "source": [
+    "h_grid = [3, 5, 8, 12, 15]\n",
+    "fits = [\n",
+    "    RegressionDiscontinuity(cutoff=CUTOFF, h=h).fit(\n",
+    "        df, outcome=\"earnings\", running=\"score\"\n",
+    "    )\n",
+    "    for h in h_grid\n",
+    "]\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(7, 4))\n",
+    "atts = [f.att for f in fits]\n",
+    "los = [f.att - f.conf_int[0] for f in fits]\n",
+    "his = [f.conf_int[1] - f.att for f in fits]\n",
+    "ax.errorbar(\n",
+    "    h_grid, atts, yerr=[los, his], fmt=\"o\", color=\"tab:blue\",\n",
+    "    ecolor=\"tab:gray\", capsize=4, lw=2,\n",
+    ")\n",
+    "ax.axhline(true_itt, color=\"tab:green\", ls=\"--\", lw=1.5,\n",
+    "           label=f\"true ITT (${true_itt:,.0f})\")\n",
+    "ax.axhline(0, color=\"k\", lw=0.8)\n",
+    "ax.axvline(sharp.h_left, color=\"tab:gray\", ls=\":\", lw=1.5,\n",
+    "           label=f\"MSE-optimal h ({sharp.h_left:.1f})\")\n",
+    "ax.set_xlabel(\"bandwidth h (score points)\")\n",
+    "ax.set_ylabel(\"estimated offer effect ($)\")\n",
+    "ax.set_title(\"One consistent story across bandwidths\")\n",
+    "ax.legend()\n",
+    "plt.show()\n",
+    "\n",
+    "for h, f in zip(h_grid, fits):\n",
+    "    print(\n",
+    "        f\"h = {h:>2}: att = ${f.att:>6,.0f}, \"\n",
+    "        f\"95% CI [${f.conf_int[0]:>6,.0f}, ${f.conf_int[1]:>6,.0f}]\"\n",
+    "    )\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Every bandwidth from 3 to 15 points tells the same story - estimates between \\$2,500\n",
+    "and \\$3,900, every interval excluding zero and covering the truth. This is\n",
+    "*specification stability*: reassuring about the estimator's tuning, though - like all\n",
+    "the checks in this act - it supports the design without being able to establish it.\n",
+    "\n",
+    "One check we have *not* run is a **manipulation (density) test** - McCrary (2008) /\n",
+    "Cattaneo, Jansson & Ma (2020) - which asks whether students bunch just above the\n",
+    "cutoff (they cannot here, because the simulation gives them no control over their\n",
+    "score). A dedicated density test is not yet packaged in `diff-diff`. The estimator\n",
+    "does handle *mass points* (repeated running-variable values) automatically via\n",
+    "`masspoints=\"adjust\"`, the same default as R - but that is a data-handling\n",
+    "adjustment, not a substitute for a manipulation test.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Act 5: fuzzy RD - the effect of enrollment itself\n",
+    "\n",
+    "The sharp estimate answers \"what did the *offer* do?\" - the right question for the\n",
+    "budget office. But the board asked what enrolling in the program does for students\n",
+    "who take it up, and only 74% of offered students enrolled (while 6% below the\n",
+    "cutoff enrolled on equivalent outside aid). Crossing the cutoff moves *take-up*\n",
+    "rather than determining it: a **fuzzy** design. Note the estimand carefully: the\n",
+    "treatment is **funded enrollment** (the `enrolled` column), not scholarship receipt\n",
+    "per se - though for the compliers the fuzzy design identifies, the enrollment the\n",
+    "offer induces *is* scholarship-funded.\n",
+    "\n",
+    "Passing the observed take-up column via `takeup=` switches the estimand to the local\n",
+    "Wald ratio - the outcome jump divided by the take-up jump. Reading that ratio as the\n",
+    "**LATE for compliers at the cutoff** (the effect on students whose enrollment the\n",
+    "offer actually changed) requires the full instrumental-variables bundle, locally:\n",
+    "continuity, a *nonzero first stage* (crossing the cutoff must actually move\n",
+    "enrollment), an *exclusion restriction* (crossing affects earnings only through\n",
+    "enrollment - true by construction in this simulation, but an assumption the\n",
+    "estimator cannot verify), and *monotonicity* (no student avoids enrolling\n",
+    "*because* they crossed). The estimator reports the ratio; the causal reading is\n",
+    "yours to defend.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-15T18:31:48.317866Z",
+     "iopub.status.busy": "2026-08-15T18:31:48.317806Z",
+     "iopub.status.idle": "2026-08-15T18:31:48.476752Z",
+     "shell.execute_reply": "2026-08-15T18:31:48.476407Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "========================================================================\n",
+      "            Fuzzy Regression Discontinuity (rdrobust parity)            \n",
+      "========================================================================\n",
+      "Cutoff:               65\n",
+      "Estimand:             fuzzy (LATE for compliers at the cutoff)\n",
+      "Kernel: triangular     Bandwidth selector: mserd\n",
+      "Order (p, q): (1, 2)       VCE: nn (nnmatch=3)   Masspoints: adjust\n",
+      "N = 8000 (4250 left / 3750 right); effective N_h = 1738/1601, N_b = 2718/2569\n",
+      "h = [6.3572, 6.3572]   b = [11.1093, 11.1093]\n",
+      "------------------------------------------------------------------------\n",
+      "             First-stage estimates (treatment take-up jump)             \n",
+      "Method                Coef.  Std. Err.        z    P>|z|[95% Conf. Int.]\n",
+      "------------------------------------------------------------------------\n",
+      "Conventional         0.6947     0.0270   25.764    0.000  [ 0.6418,  0.7475]\n",
+      "Bias-Corrected       0.7009     0.0270   25.994    0.000  [ 0.6480,  0.7537]\n",
+      "Robust               0.7009     0.0312   22.497    0.000  [ 0.6398,  0.7619]\n",
+      "------------------------------------------------------------------------\n",
+      "                       Treatment effect estimates                       \n",
+      "Method                Coef.  Std. Err.        z    P>|z|[95% Conf. Int.]\n",
+      "------------------------------------------------------------------------\n",
+      "Conventional      3836.9953   797.9523    4.809    0.000  [2273.0376, 5400.9531]\n",
+      "Bias-Corrected    3765.2237   797.9523    4.719    0.000  [2201.2659, 5329.1815]\n",
+      "Robust            3765.2237   925.3021    4.069    0.000  [1951.6648, 5578.7826]\n",
+      "------------------------------------------------------------------------\n",
+      "Note: canonical att/se/t_stat/p_value/conf_int are the ROBUST row\n",
+      "(att = bias-corrected estimate; rdrobust prints the conventional\n",
+      "estimate as its headline coefficient - see att_conventional).\n",
+      "========================================================================\n"
+     ]
+    }
+   ],
+   "source": [
+    "fuzzy = RegressionDiscontinuity(cutoff=CUTOFF).fit(\n",
+    "    df, outcome=\"earnings\", running=\"score\", takeup=\"enrolled\"\n",
+    ")\n",
+    "print(fuzzy.summary())\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Two blocks now:\n",
+    "\n",
+    "- **First stage**: crossing the cutoff raises enrollment by **70 percentage points**\n",
+    "  (0.701, robust CI [0.64, 0.76]) - comfortably strong, matching the designed\n",
+    "  0.72 - 0.06 = 0.66 jump. A weak first stage (a take-up jump near zero) would make\n",
+    "  the ratio explode; always read this block first.\n",
+    "- **Treatment effect**: the complier LATE is **\\$3,765, CI [\\$1,952, \\$5,579]** -\n",
+    "  covering the \\$4,000 truth. The interval is wider than the sharp one *here*, as\n",
+    "  fuzzy intervals often are (the first stage is estimated and below one) - though\n",
+    "  the two target different-scaled parameters, so their relative widths are not\n",
+    "  mechanically ordered.\n",
+    "- The two estimands follow the classic IV arithmetic approximately - ITT ≈ first\n",
+    "  stage x LATE (\\$2,629 ≈ 0.70 x \\$3,765 = \\$2,637 here). The reported numbers do\n",
+    "  not obey it exactly, for two reasons: the fuzzy fit selects its own bandwidth for\n",
+    "  the ratio objective rather than reusing the sharp one, and the robust row\n",
+    "  bias-corrects the *first-order linearization of the ratio* (CCT 2014,\n",
+    "  Section 3.2) rather than dividing separately bias-corrected numerator and\n",
+    "  denominator.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Act 6: covariates buy precision, not identification\n",
+    "\n",
+    "In this library's DiD estimators, `covariates` changes the *identifying assumption*\n",
+    "(to conditional parallel trends). RD covariates do nothing of the sort: the CCFT\n",
+    "(2019) adjustment enters additively with a common coefficient pooled across sides,\n",
+    "the estimand is **unchanged**, and the payoff - when there is one - is a **shorter\n",
+    "confidence interval**. The gain is not automatic: CCFT's guarantee - an asymptotic variance no\n",
+    "larger than the unadjusted estimator's - holds when the covariates' best linear\n",
+    "association with the outcome is the same on both sides of the cutoff (the\n",
+    "common-coefficient specification), and the realized gain depends on how much outcome\n",
+    "variance the covariates absorb near the cutoff.\n",
+    "\n",
+    "Balance at the cutoff is the recommended *testable sufficient condition* for\n",
+    "adjustment to leave the estimand unchanged - supported by Act 4a's falsification\n",
+    "checks, and true by construction in this simulation. Under imbalance the adjusted\n",
+    "estimator's probability limit shifts by the covariate jumps weighted by their\n",
+    "outcome coefficients, so adjustment is generally inconsistent unless that product\n",
+    "happens to be zero - and \"adjusting for\" imbalance cannot repair a broken design.\n",
+    "Balance first, then adjust.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-15T18:31:48.477922Z",
+     "iopub.status.busy": "2026-08-15T18:31:48.477861Z",
+     "iopub.status.idle": "2026-08-15T18:31:48.711850Z",
+     "shell.execute_reply": "2026-08-15T18:31:48.711500Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "unadjusted: att = $2,629, 95% CI [$1,601, $3,657] (width $2,056)\n",
+      "  adjusted: att = $2,581, 95% CI [$1,834, $3,329] (width $1,495)\n",
+      "CI shortened by 27%\n"
+     ]
+    }
+   ],
+   "source": [
+    "adjusted = RegressionDiscontinuity(cutoff=CUTOFF).fit(\n",
+    "    df,\n",
+    "    outcome=\"earnings\",\n",
+    "    running=\"score\",\n",
+    "    covariates=[\"parental_income\", \"hs_gpa\"],\n",
+    ")\n",
+    "\n",
+    "for label, fit in ((\"unadjusted\", sharp), (\"adjusted\", adjusted)):\n",
+    "    width = fit.conf_int[1] - fit.conf_int[0]\n",
+    "    print(\n",
+    "        f\"{label:>10}: att = ${fit.att:,.0f}, \"\n",
+    "        f\"95% CI [${fit.conf_int[0]:,.0f}, ${fit.conf_int[1]:,.0f}] \"\n",
+    "        f\"(width ${width:,.0f})\"\n",
+    "    )\n",
+    "shrink = 1 - (adjusted.conf_int[1] - adjusted.conf_int[0]) / (\n",
+    "    sharp.conf_int[1] - sharp.conf_int[0]\n",
+    ")\n",
+    "print(f\"CI shortened by {shrink:.0%}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Same estimand, same conclusion, and - in this simulation - a **27% shorter\n",
+    "interval**: the covariates soak up outcome variance that was pure noise for the jump. Bandwidths are covariate-aware\n",
+    "(the adjustment propagates into bandwidth selection, as in R), and collinear\n",
+    "covariates are dropped with a warning naming them under the default\n",
+    "`covs_drop=True`.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Takeaways\n",
+    "\n",
+    "- **The illusion was worth \\$12,000.** The naive above-vs-below comparison said\n",
+    "  \\$14,779; the design-based answer is \\$2,629 for the offer (truth: \\$2,640) and\n",
+    "  \\$3,765 for compliers who enroll (truth: \\$4,000). Selection on the running\n",
+    "  variable, not the scholarship, drove the gap.\n",
+    "- **Plot first.** `RDPlot` makes both the confound (the smooth climb) and the effect\n",
+    "  (the jump at 65) visible before any estimation - and matches R's `rdplot()`\n",
+    "  numbers on the supported surface.\n",
+    "- **Report the Robust row.** `diff-diff` binds `att`/`se`/`conf_int` to the robust\n",
+    "  bias-corrected inference of CCT (2014) - the row whose coverage is asymptotically\n",
+    "  valid under the method's conditions - while `summary()` prints all three rows and\n",
+    "  `att_conventional` keeps the conventional estimate available.\n",
+    "- **Falsification checks are built in.** Covariate balance (p = 0.99, 0.27), placebo\n",
+    "  cutoffs (p = 0.33, 0.74), and a bandwidth sweep that never moves the story - all\n",
+    "  with the same estimator, no extra machinery. They support the design's\n",
+    "  assumptions; continuity itself remains untestable.\n",
+    "- **Sharp vs fuzzy is offer vs treatment.** `takeup=` turns the cutoff into an\n",
+    "  instrument for enrollment; the first-stage block tells you whether that\n",
+    "  instrument is strong enough to lean on.\n",
+    "- **RD covariates ≠ DiD covariates.** Here they shortened the CI by 27% while\n",
+    "  leaving the estimand and the substantive conclusion unchanged (the finite-sample\n",
+    "  point estimate and the selected bandwidth can move) - precision, never\n",
+    "  identification.\n",
+    "\n",
+    "### References\n",
+    "\n",
+    "- Calonico, S., Cattaneo, M. D., & Titiunik, R. (2014). Robust Nonparametric\n",
+    "  Confidence Intervals for Regression-Discontinuity Designs. *Econometrica*, 82(6),\n",
+    "  2295-2326.\n",
+    "- Calonico, S., Cattaneo, M. D., & Titiunik, R. (2015). Optimal Data-Driven\n",
+    "  Regression Discontinuity Plots. *Journal of the American Statistical Association*,\n",
+    "  110(512), 1753-1769.\n",
+    "- Calonico, S., Cattaneo, M. D., Farrell, M. H., & Titiunik, R. (2019). Regression\n",
+    "  Discontinuity Designs Using Covariates. *Review of Economics and Statistics*,\n",
+    "  101(3), 442-451.\n",
+    "- Cattaneo, M. D., Idrobo, N., & Titiunik, R. (2020). *A Practical Introduction to\n",
+    "  Regression Discontinuity Designs: Foundations*. Cambridge University Press.\n",
+    "- Cattaneo, M. D., Jansson, M., & Ma, X. (2020). Simple Local Polynomial Density\n",
+    "  Estimators. *Journal of the American Statistical Association*, 115(531),\n",
+    "  1449-1455.\n",
+    "- McCrary, J. (2008). Manipulation of the running variable in the regression\n",
+    "  discontinuity design: A density test. *Journal of Econometrics*, 142(2),\n",
+    "  698-714.\n",
+    "- Thistlethwaite, D. L., & Campbell, D. T. (1960). Regression-discontinuity analysis:\n",
+    "  An alternative to the ex post facto experiment. *Journal of Educational\n",
+    "  Psychology*, 51(6), 309-317.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.14.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/docs/tutorials/index.rst b/docs/tutorials/index.rst
index 6fa205e45..f484960d6 100644
--- a/docs/tutorials/index.rst
+++ b/docs/tutorials/index.rst
@@ -215,6 +215,13 @@ Modern estimators for designs the basic toolkit cannot handle.
 
       Single-treated-unit policy evaluation with two routes to inference.
 
+   .. grid-item-card:: Regression Discontinuity
+      :link: 28_rdd_scholarship_illusion
+      :link-type: doc
+
+      Sharp and fuzzy RD from plot to estimate, when a naive cutoff
+      comparison overstates the effect fivefold.
+
 .. toctree::
    :maxdepth: 1
    :caption: Advanced Methods
@@ -229,6 +236,7 @@ Modern estimators for designs the basic toolkit cannot handle.
    Survey-Aware DiD <16_survey_did>
    Wooldridge ETWFE <16_wooldridge_etwfe>
    Synthetic Control for Policy <25_synthetic_control_policy>
+   Regression Discontinuity (RDD) <28_rdd_scholarship_illusion>
 
 Study Design
 ------------
diff --git a/tests/test_t28_rdd_scholarship_illusion_drift.py b/tests/test_t28_rdd_scholarship_illusion_drift.py
new file mode 100644
index 000000000..68979d18e
--- /dev/null
+++ b/tests/test_t28_rdd_scholarship_illusion_drift.py
@@ -0,0 +1,328 @@
+"""Drift detection for Tutorial 28
+(``docs/tutorials/28_rdd_scholarship_illusion.ipynb``).
+
+The tutorial's narrative rests on these quantitative claims:
+
+1. **The illusion.** The naive above-vs-below-cutoff earnings gap is
+   ~$14,779, versus a true offer ITT of $2,640 by construction
+   ((0.72 - 0.06) x $4,000).
+2. **Sharp RD nails the ITT.** The robust bias-corrected offer effect is
+   ~$2,629 with CI [$1,601, $3,657], at the mserd-selected h ~ 10.62.
+3. **The validity toolkit is quiet.** Balance jumps for parental income
+   (~$20, p ~ 0.99) and GPA (~-0.02, p ~ 0.27); placebo cutoffs at the
+   within-side medians find nothing (p ~ 0.33 / 0.74); the manual-h sweep
+   {3, 5, 8, 12, 15} stays between ~$2.5k and ~$3.9k with every CI
+   excluding zero.
+4. **Fuzzy RD recovers the enrollment effect.** First stage ~0.701;
+   complier LATE ~$3,765 with CI [$1,952, $5,579], covering the $4,000
+   truth.
+5. **Covariates buy precision.** The adjusted fit reports ~$2,581 with a
+   ~27% shorter CI than the unadjusted sharp fit.
+
+``nbsphinx_execute = "never"`` means RTD renders the committed outputs, so
+CI cannot detect drift by re-executing the notebook. These tests re-derive
+the load-bearing numbers from the same public API the notebook uses and
+cross-check the rendered surface (markdown + committed outputs), so library
+drift or notebook-only edits fail loudly.
+
+All fits here are single deterministic local-polynomial fits (no
+bootstrap), so nothing needs ``ci_params`` scaling or a slow marker.
+"""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import pytest
+
+from diff_diff import RDPlot, RegressionDiscontinuity
+from tests._tutorial_drift import assert_quotes_in_rendered
+
+NB = "docs/tutorials/28_rdd_scholarship_illusion.ipynb"
+
+# Locked design - must stay in sync with the notebook's DGP cell
+# (cross-checked by ``test_notebook_constants_match``).
+SEED = 25
+N = 8_000
+CUTOFF = 65.0
+TRUE_EFFECT = 4_000.0
+P_ENROLL_IF_OFFERED = 0.72
+P_ALWAYS = 0.06
+TRUE_ITT = (P_ENROLL_IF_OFFERED - P_ALWAYS) * TRUE_EFFECT  # 2640.0
+
+H_GRID = (3, 5, 8, 12, 15)
+
+# Single-fit local-polynomial estimates: cross-OS BLAS variation is
+# ULP-scale (see .claude/memory.md tolerance notes), so dollar-scale pins
+# use abs=0.01 and probability-scale pins abs=1e-6.
+DOLLAR_TOL = 0.01
+P_TOL = 1e-6
+
+
+def make_cohort() -> pd.DataFrame:
+    """Faithful copy of the notebook's DGP cell."""
+    rng = np.random.default_rng(SEED)
+    ability = rng.normal(0.0, 1.0, N)
+    score = np.clip(64 + 11 * ability + rng.normal(0, 4, N), 30, 100)
+    parental_income = np.clip(45_000 + 600 * (score - 60) + rng.normal(0, 15_000, N), 8_000, None)
+    hs_gpa = np.clip(2.9 + 0.016 * (score - 60) + rng.normal(0, 0.30, N), 0.0, 4.0)
+    offer = (score >= CUTOFF).astype(int)
+    u = rng.uniform(size=N)
+    enrolled = np.where(offer == 1, (u < P_ENROLL_IF_OFFERED), (u < P_ALWAYS)).astype(int)
+    base = 24_000 + 320 * score + 7.0 * (score - 60) ** 2
+    earnings = (
+        base
+        + 0.35 * (parental_income - 45_000)
+        + 4_000 * (hs_gpa - 2.9)
+        + TRUE_EFFECT * enrolled
+        + rng.normal(0, 4_500, N)
+    )
+    return pd.DataFrame(
+        {
+            "score": score,
+            "offer": offer,
+            "enrolled": enrolled,
+            "earnings": earnings,
+            "parental_income": parental_income,
+            "hs_gpa": hs_gpa,
+        }
+    )
+
+
+@pytest.fixture(scope="module")
+def df() -> pd.DataFrame:
+    return make_cohort()
+
+
+@pytest.fixture(scope="module")
+def sharp(df):
+    return RegressionDiscontinuity(cutoff=CUTOFF).fit(df, outcome="earnings", running="score")
+
+
+class TestTutorial28Drift:
+    def test_naive_gap_is_the_illusion(self, df):
+        naive = df.loc[df.offer == 1, "earnings"].mean() - df.loc[df.offer == 0, "earnings"].mean()
+        assert naive == pytest.approx(14778.9046, abs=DOLLAR_TOL)
+        # the story: the naive gap overstates the true ITT >5x
+        assert naive > 5 * TRUE_ITT
+
+    def test_sharp_rd_pins(self, sharp):
+        assert sharp.att == pytest.approx(2628.7444, abs=DOLLAR_TOL)
+        assert sharp.se == pytest.approx(524.4613, abs=DOLLAR_TOL)
+        assert sharp.conf_int[0] == pytest.approx(1600.8193, abs=DOLLAR_TOL)
+        assert sharp.conf_int[1] == pytest.approx(3656.6696, abs=DOLLAR_TOL)
+        assert sharp.att_conventional == pytest.approx(2616.1764, abs=DOLLAR_TOL)
+        assert sharp.h_left == pytest.approx(10.6211, abs=1e-4)
+        # the CI covers the designed truth
+        assert sharp.conf_int[0] < TRUE_ITT < sharp.conf_int[1]
+
+    def test_balance_pins(self, df):
+        rd = RegressionDiscontinuity(cutoff=CUTOFF)
+        income = rd.fit(df, outcome="parental_income", running="score")
+        assert income.att == pytest.approx(20.1542, abs=DOLLAR_TOL)
+        assert income.p_value == pytest.approx(0.986901, abs=P_TOL)
+        gpa = rd.fit(df, outcome="hs_gpa", running="score")
+        assert gpa.att == pytest.approx(-0.023270, abs=1e-5)
+        assert gpa.p_value == pytest.approx(0.272935, abs=P_TOL)
+
+    def test_placebo_pins(self, df):
+        left = df[df.score < CUTOFF]
+        right = df[df.score >= CUTOFF]
+        pins = [
+            (left, 56.9195, 1161.7591, 0.333126),
+            (right, 72.6014, -349.1510, 0.742107),
+        ]
+        for side, fake_median, att, p in pins:
+            fake = float(side.score.median())
+            assert fake == pytest.approx(fake_median, abs=1e-4)
+            fit = RegressionDiscontinuity(cutoff=fake).fit(
+                side, outcome="earnings", running="score"
+            )
+            assert fit.att == pytest.approx(att, abs=DOLLAR_TOL)
+            assert fit.p_value == pytest.approx(p, abs=P_TOL)
+
+    def test_bandwidth_sweep_pins(self, df):
+        pins = {
+            3: (3905.1486, 1394.9944, 6415.3028),
+            5: (3341.4404, 1441.2065, 5241.6743),
+            8: (3005.2456, 1526.5811, 4483.9102),
+            12: (2533.9585, 1335.5907, 3732.3262),
+            15: (2606.9459, 1528.4022, 3685.4897),
+        }
+        for h in H_GRID:
+            att, lo, hi = pins[h]
+            fit = RegressionDiscontinuity(cutoff=CUTOFF, h=h).fit(
+                df, outcome="earnings", running="score"
+            )
+            assert fit.att == pytest.approx(att, abs=DOLLAR_TOL)
+            assert fit.conf_int[0] == pytest.approx(lo, abs=DOLLAR_TOL)
+            assert fit.conf_int[1] == pytest.approx(hi, abs=DOLLAR_TOL)
+            # the prose claims every interval excludes zero AND covers the
+            # designed truth
+            assert fit.conf_int[0] > 0
+            assert fit.conf_int[0] < TRUE_ITT < fit.conf_int[1]
+
+    def test_rdplot_acts_pins(self, df):
+        # Act 2's two fits: full support (defaults) and the p=2 zoom with
+        # per-bin CIs, exactly as configured in the notebook.
+        full = RDPlot(cutoff=CUTOFF).fit(df, outcome="earnings", running="score")
+        assert full.J == (123.0, 163.0)
+        assert not full.ci_requested
+
+        window = df[df.score.between(CUTOFF - 10, CUTOFF + 10)]
+        assert len(window) == 4890
+        zoom_est = RDPlot(cutoff=CUTOFF, p=2, ci=95)
+        assert zoom_est.p == 2
+        assert zoom_est.ci == 95
+        zoom = zoom_est.fit(window, outcome="earnings", running="score")
+        assert zoom.J == (72.0, 76.0)
+        assert zoom.ci_requested
+
+        # populated bin rows: the full fit drops empty tail bins (276 of the
+        # 123+163 selected), the zoom window populates every bin (72+76)
+        for res, n_rows in ((full, 276), (zoom, 148)):
+            vb = res.vars_bins
+            means = np.asarray(vb["rdplot_mean_y"], dtype=float)
+            ci_l = np.asarray(vb["rdplot_ci_l"], dtype=float)
+            ci_r = np.asarray(vb["rdplot_ci_r"], dtype=float)
+            assert len(means) == n_rows
+            assert np.isfinite(means).all()
+            assert np.isfinite(ci_l).all() and np.isfinite(ci_r).all()
+
+    def test_rdplot_renders_noninteractive(self, df):
+        matplotlib = pytest.importorskip("matplotlib")
+        matplotlib.use("Agg")
+        import matplotlib.pyplot as plt
+
+        zoom = RDPlot(cutoff=CUTOFF, p=2, ci=95).fit(
+            df[df.score.between(CUTOFF - 10, CUTOFF + 10)],
+            outcome="earnings",
+            running="score",
+        )
+        ax = zoom.plot(title="smoke", xlabel="score", ylabel="earnings")
+        assert ax.get_title() == "smoke"
+        plt.close(ax.figure)
+
+    def test_fuzzy_pins(self, df, sharp):
+        fuzzy = RegressionDiscontinuity(cutoff=CUTOFF).fit(
+            df, outcome="earnings", running="score", takeup="enrolled"
+        )
+        assert fuzzy.estimand == "fuzzy (LATE for compliers at the cutoff)"
+        assert fuzzy.first_stage is not None
+        assert fuzzy.first_stage == pytest.approx(0.700885, abs=P_TOL)
+        assert fuzzy.att == pytest.approx(3765.2237, abs=DOLLAR_TOL)
+        assert fuzzy.conf_int[0] == pytest.approx(1951.6648, abs=DOLLAR_TOL)
+        assert fuzzy.conf_int[1] == pytest.approx(5578.7826, abs=DOLLAR_TOL)
+        # covers the designed enrollment effect
+        assert fuzzy.conf_int[0] < TRUE_EFFECT < fuzzy.conf_int[1]
+        # the tutorial's IV-arithmetic claim: approximate, not exact
+        # (different bandwidths + linearized ratio bias correction)
+        product = fuzzy.first_stage * fuzzy.att
+        assert product == pytest.approx(sharp.att, rel=0.01)
+        assert abs(product - sharp.att) > 1.0
+
+    def test_covariate_adjustment_pins(self, df, sharp):
+        adj = RegressionDiscontinuity(cutoff=CUTOFF).fit(
+            df,
+            outcome="earnings",
+            running="score",
+            covariates=["parental_income", "hs_gpa"],
+        )
+        assert adj.att == pytest.approx(2581.2134, abs=DOLLAR_TOL)
+        assert adj.conf_int[0] == pytest.approx(1833.8958, abs=DOLLAR_TOL)
+        assert adj.conf_int[1] == pytest.approx(3328.5311, abs=DOLLAR_TOL)
+        shrink = 1 - (adj.conf_int[1] - adj.conf_int[0]) / (sharp.conf_int[1] - sharp.conf_int[0])
+        # the "27% shorter CI in this simulation" claim
+        assert round(shrink * 100) == 27
+
+    @staticmethod
+    def _notebook_code_cells() -> list:
+        nb_path = Path(__file__).resolve().parents[1] / NB
+        if not nb_path.exists():
+            pytest.skip(
+                f"Notebook {NB!r} not available in this CI environment "
+                "(isolated-install job copies only tests/, not docs/)."
+            )
+        nb = json.loads(nb_path.read_text())
+        return [
+            "".join(c["source"]) if isinstance(c["source"], list) else c["source"]
+            for c in nb["cells"]
+            if c["cell_type"] == "code"
+        ]
+
+    def test_notebook_dgp_cell_reproduces_test_cohort(self, df, capsys):
+        """Full-DGP synchronization: execute the notebook's actual DGP cell
+        and require its DataFrame to equal ``make_cohort()`` exactly.
+
+        This locks EVERY term of the simulation (score parameters, baseline
+        slope, earnings coefficients, ...), not a fragment allowlist - an
+        edit to either copy that changes the data fails here.
+        """
+        cells = self._notebook_code_cells()
+        dgp_cells = [c for c in cells if "SEED = 25" in c]
+        assert len(dgp_cells) == 1, "expected exactly one DGP cell"
+        ns: dict = {"np": np, "pd": pd}
+        exec(dgp_cells[0], ns)  # noqa: S102 - trusted repo-committed notebook
+        capsys.readouterr()  # swallow the cell's print() output
+        pd.testing.assert_frame_equal(ns["df"], df)
+        assert ns["true_itt"] == TRUE_ITT
+
+    def test_notebook_fit_configs_match(self):
+        """The fit-configuration surface outside the DGP cell."""
+        src = "\n".join(self._notebook_code_cells())
+        for needle in (
+            "h_grid = [3, 5, 8, 12, 15]",
+            'takeup="enrolled"',
+            'covariates=["parental_income", "hs_gpa"]',
+            "RDPlot(cutoff=CUTOFF)",
+            "RDPlot(cutoff=CUTOFF, p=2, ci=95)",
+            "df[df.score.between(CUTOFF - 10, CUTOFF + 10)]",
+        ):
+            assert needle in src, f"notebook drifted from locked config: {needle!r}"
+
+    def test_rendered_surface_quotes(self):
+        # Markdown prose quotes (reader-facing claims)...
+        assert_quotes_in_rendered(
+            NB,
+            [
+                "**\\$2,640**",  # the designed offer ITT
+                "\\$2,629",  # sharp robust estimate in prose
+                "**\\$3,765, CI [\\$1,952, \\$5,579]**",  # fuzzy LATE claim
+                "70 percentage points",  # first-stage reading
+                "p = 0.99",  # balance: parental income
+                "p = 0.33 and 0.74",  # placebos
+                "27% shorter",  # covariate payoff, scoped to the simulation
+                "no balance test can prove continuity",  # falsification framing
+                "mechanically ordered",  # fuzzy-CI width caveat
+                "asymptotically valid",  # RBC coverage claim (not "honest")
+                "testable sufficient condition",  # balance role (not necessity)
+                "for this illustration",  # placebo medians are illustrative
+                "funded enrollment",  # the treatment is enrollment, not
+                # scholarship receipt (CI review)
+                "`b = h`",  # h-only sweep moves the bias bandwidth too
+                "first-order linearization of the ratio",  # RBC ratio caveat
+            ],
+            surface="markdown",
+        )
+        # ...and the executed-output numbers they round from.
+        assert_quotes_in_rendered(
+            NB,
+            [
+                "naive above-vs-below gap: $14,779",
+                "offer effect (robust bias-corrected): $2,629",
+                "95% CI: [$1,601, $3,657]",
+                "jump at cutoff =   20.154",  # balance: parental income
+                "p = 0.987",
+                "placebo cutoff 56.9",
+                "placebo cutoff 72.6",
+                "h =  3: att = $ 3,905",
+                "h = 15: att = $ 2,607",
+                "0.7009",  # first stage (summary table)
+                "3765.2237",  # fuzzy robust estimate (summary table)
+                "CI shortened by 27%",
+            ],
+            surface="output",
+        )