{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "41cc1632",
   "metadata": {},
   "source": [
    "# Synthetic Control in Python: Policy Evaluation Step by Step\n",
    "\n",
    "## Complete analysis notebook\n",
    "\n",
    "This notebook reproduces the worked analysis used in the article.\n",
    "\n",
    "It uses a **simulated US state panel** for teaching. Colorado is the treated state and the simulated policy begins in 2018.\n",
    "\n",
    "The results are not official Colorado statistics and should not be presented as evidence about a real policy.\n",
    "\n",
    "The notebook includes:\n",
    "\n",
    "1. Simulated panel creation\n",
    "2. Data checks\n",
    "3. Donor screening\n",
    "4. Classic synthetic control estimation\n",
    "5. Pre treatment fit\n",
    "6. Donor weights\n",
    "7. Predictor balance\n",
    "8. Policy effect estimation\n",
    "9. In space placebo tests\n",
    "10. Placebo gap paths\n",
    "11. Robustness checks\n",
    "12. Interactive Plotly charts\n",
    "13. Animated donor buildup\n",
    "14. CSV export\n",
    "15. Final result summary\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8db1dcc0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-03T17:23:12.490249Z",
     "iopub.status.busy": "2026-09-03T17:23:12.490124Z",
     "iopub.status.idle": "2026-09-03T17:23:13.114126Z",
     "shell.execute_reply": "2026-09-03T17:23:13.113521Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python: 3.13.5\n",
      "Platform: Linux-6.18.35-x86_64-with-glibc2.41\n",
      "NumPy: 2.3.5\n",
      "pandas: 2.2.3\n",
      "Plotly available: True\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import platform\n",
    "from pathlib import Path\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.optimize import minimize\n",
    "\n",
    "try:\n",
    "    import plotly.graph_objects as go\n",
    "    PLOTLY_AVAILABLE = True\n",
    "except Exception:\n",
    "    PLOTLY_AVAILABLE = False\n",
    "\n",
    "OUTPUT_DIR = Path(\"synthetic_control_outputs\")\n",
    "OUTPUT_DIR.mkdir(exist_ok=True)\n",
    "\n",
    "print(\"Python:\", sys.version.split()[0])\n",
    "print(\"Platform:\", platform.platform())\n",
    "print(\"NumPy:\", np.__version__)\n",
    "print(\"pandas:\", pd.__version__)\n",
    "print(\"Plotly available:\", PLOTLY_AVAILABLE)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40359f5d",
   "metadata": {},
   "source": [
    "## 1. Create the simulated US state panel\n",
    "\n",
    "The panel runs from 2005 through 2024. Colorado receives the simulated treatment in 2018.\n",
    "\n",
    "The outcome is a simulated renewable generation index. Three simulated predictors describe income, industrial share, and electricity price conditions.\n",
    "\n",
    "A known treatment effect is added only after 2018 so the workflow has a clear teaching signal.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "41ec28e2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-03T17:23:13.115726Z",
     "iopub.status.busy": "2026-09-03T17:23:13.115476Z",
     "iopub.status.idle": "2026-09-03T17:23:13.147797Z",
     "shell.execute_reply": "2026-09-03T17:23:13.147369Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows: 440\n",
      "States: 22\n",
      "Years: 2005 to 2024\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>year</th>\n",
       "      <th>renewable_index</th>\n",
       "      <th>treatment</th>\n",
       "      <th>treated_state</th>\n",
       "      <th>income_index</th>\n",
       "      <th>industrial_share</th>\n",
       "      <th>electricity_price_index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Colorado</td>\n",
       "      <td>2005</td>\n",
       "      <td>86.100560</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>106.294655</td>\n",
       "      <td>21.755629</td>\n",
       "      <td>105.35157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Colorado</td>\n",
       "      <td>2006</td>\n",
       "      <td>88.012127</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>106.294655</td>\n",
       "      <td>21.755629</td>\n",
       "      <td>105.35157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Colorado</td>\n",
       "      <td>2007</td>\n",
       "      <td>90.067322</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>106.294655</td>\n",
       "      <td>21.755629</td>\n",
       "      <td>105.35157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Colorado</td>\n",
       "      <td>2008</td>\n",
       "      <td>91.796039</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>106.294655</td>\n",
       "      <td>21.755629</td>\n",
       "      <td>105.35157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Colorado</td>\n",
       "      <td>2009</td>\n",
       "      <td>92.075025</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>106.294655</td>\n",
       "      <td>21.755629</td>\n",
       "      <td>105.35157</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      state  year  ...  industrial_share  electricity_price_index\n",
       "0  Colorado  2005  ...         21.755629                105.35157\n",
       "1  Colorado  2006  ...         21.755629                105.35157\n",
       "2  Colorado  2007  ...         21.755629                105.35157\n",
       "3  Colorado  2008  ...         21.755629                105.35157\n",
       "4  Colorado  2009  ...         21.755629                105.35157\n",
       "\n",
       "[5 rows x 8 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "rng = np.random.default_rng(123)\n",
    "\n",
    "years = np.arange(2005, 2025)\n",
    "policy_year = 2018\n",
    "treated = \"Colorado\"\n",
    "\n",
    "donors = [\n",
    "    \"Arizona\", \"Arkansas\", \"Connecticut\", \"Idaho\", \"Illinois\", \"Indiana\",\n",
    "    \"Iowa\", \"Kansas\", \"Kentucky\", \"Maine\", \"Michigan\", \"Minnesota\",\n",
    "    \"Mississippi\", \"Missouri\", \"Nevada\", \"New Mexico\", \"North Carolina\",\n",
    "    \"Oregon\", \"Utah\", \"Virginia\", \"Wisconsin\"\n",
    "]\n",
    "\n",
    "units = [treated] + donors\n",
    "T = len(years)\n",
    "nD = len(donors)\n",
    "t = np.arange(T)\n",
    "\n",
    "f1 = 0.7 * t + 2 * np.sin(t / 2.8)\n",
    "f2 = np.cos(t / 3.4) * 3 + 0.15 * t\n",
    "f3 = np.sin(t / 5.3) * 2\n",
    "\n",
    "load1 = rng.uniform(0.8, 1.3, nD)\n",
    "load2 = rng.uniform(-0.6, 0.8, nD)\n",
    "load3 = rng.uniform(-0.4, 0.7, nD)\n",
    "base = rng.uniform(70, 110, nD)\n",
    "trend = rng.uniform(-0.1, 0.35, nD)\n",
    "\n",
    "Yd = np.zeros((T, nD))\n",
    "\n",
    "for j in range(nD):\n",
    "    noise = rng.normal(0, 0.8, T)\n",
    "    Yd[:, j] = (\n",
    "        base[j]\n",
    "        + load1[j] * f1\n",
    "        + load2[j] * f2\n",
    "        + load3[j] * f3\n",
    "        + trend[j] * t\n",
    "        + noise\n",
    "    )\n",
    "\n",
    "true_weights = {\n",
    "    \"Arizona\": 0.26,\n",
    "    \"Idaho\": 0.22,\n",
    "    \"Iowa\": 0.18,\n",
    "    \"New Mexico\": 0.14,\n",
    "    \"Wisconsin\": 0.12,\n",
    "    \"Minnesota\": 0.08,\n",
    "}\n",
    "\n",
    "wtrue = np.array([true_weights.get(d, 0.0) for d in donors])\n",
    "\n",
    "counterfactual = Yd @ wtrue\n",
    "treated_y = counterfactual + rng.normal(0, 0.5, T)\n",
    "\n",
    "effect = np.zeros(T)\n",
    "post_mask = years >= policy_year\n",
    "effect[post_mask] = np.array([3, 5, 7, 9, 11, 13, 15])\n",
    "\n",
    "treated_y = treated_y + effect\n",
    "\n",
    "pred_names = [\n",
    "    \"income_index\",\n",
    "    \"industrial_share\",\n",
    "    \"electricity_price_index\",\n",
    "]\n",
    "\n",
    "donor_preds = pd.DataFrame(\n",
    "    {\n",
    "        \"income_index\": rng.normal(105, 10, nD),\n",
    "        \"industrial_share\": rng.normal(18, 4, nD),\n",
    "        \"electricity_price_index\": rng.normal(100, 12, nD),\n",
    "    },\n",
    "    index=donors,\n",
    ")\n",
    "\n",
    "treated_preds = (\n",
    "    donor_preds.mul(wtrue, axis=0).sum(axis=0)\n",
    "    + pd.Series(\n",
    "        rng.normal(0, [1.0, 0.5, 1.0], 3),\n",
    "        index=pred_names,\n",
    "    )\n",
    ")\n",
    "\n",
    "all_preds = pd.concat(\n",
    "    [treated_preds.to_frame().T.rename(index={0: treated}), donor_preds]\n",
    ").loc[units]\n",
    "\n",
    "Yall = np.column_stack([treated_y, Yd])\n",
    "pre_mask = years < policy_year\n",
    "\n",
    "records = []\n",
    "\n",
    "for i, unit in enumerate(units):\n",
    "    for k, year in enumerate(years):\n",
    "        records.append(\n",
    "            {\n",
    "                \"state\": unit,\n",
    "                \"year\": int(year),\n",
    "                \"renewable_index\": float(Yall[k, i]),\n",
    "                \"treatment\": int(unit == treated and year >= policy_year),\n",
    "                \"treated_state\": int(unit == treated),\n",
    "                \"income_index\": float(all_preds.loc[unit, \"income_index\"]),\n",
    "                \"industrial_share\": float(all_preds.loc[unit, \"industrial_share\"]),\n",
    "                \"electricity_price_index\": float(all_preds.loc[unit, \"electricity_price_index\"]),\n",
    "            }\n",
    "        )\n",
    "\n",
    "panel = pd.DataFrame(records)\n",
    "\n",
    "print(\"Rows:\", len(panel))\n",
    "print(\"States:\", panel[\"state\"].nunique())\n",
    "print(\"Years:\", panel[\"year\"].min(), \"to\", panel[\"year\"].max())\n",
    "panel.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "921b5645",
   "metadata": {},
   "source": [
    "## 2. Inspect the data before fitting\n",
    "\n",
    "These checks confirm complete coverage, unique state and year rows, and correct treatment timing.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b2bbb693",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-03T17:23:13.149511Z",
     "iopub.status.busy": "2026-09-03T17:23:13.149386Z",
     "iopub.status.idle": "2026-09-03T17:23:13.162411Z",
     "shell.execute_reply": "2026-09-03T17:23:13.161958Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Any duplicate state and year rows: False\n",
      "Treatment rows outside Colorado: 0\n",
      "Colorado treatment start: 2018\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>first_year</th>\n",
       "      <th>last_year</th>\n",
       "      <th>observations</th>\n",
       "      <th>missing_outcome</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Arizona</th>\n",
       "      <td>2005</td>\n",
       "      <td>2024</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arkansas</th>\n",
       "      <td>2005</td>\n",
       "      <td>2024</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>2005</td>\n",
       "      <td>2024</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Connecticut</th>\n",
       "      <td>2005</td>\n",
       "      <td>2024</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Idaho</th>\n",
       "      <td>2005</td>\n",
       "      <td>2024</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>2005</td>\n",
       "      <td>2024</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indiana</th>\n",
       "      <td>2005</td>\n",
       "      <td>2024</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iowa</th>\n",
       "      <td>2005</td>\n",
       "      <td>2024</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kansas</th>\n",
       "      <td>2005</td>\n",
       "      <td>2024</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kentucky</th>\n",
       "      <td>2005</td>\n",
       "      <td>2024</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             first_year  last_year  observations  missing_outcome\n",
       "state                                                            \n",
       "Arizona            2005       2024            20                0\n",
       "Arkansas           2005       2024            20                0\n",
       "Colorado           2005       2024            20                0\n",
       "Connecticut        2005       2024            20                0\n",
       "Idaho              2005       2024            20                0\n",
       "Illinois           2005       2024            20                0\n",
       "Indiana            2005       2024            20                0\n",
       "Iowa               2005       2024            20                0\n",
       "Kansas             2005       2024            20                0\n",
       "Kentucky           2005       2024            20                0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coverage = (\n",
    "    panel.groupby(\"state\")\n",
    "    .agg(\n",
    "        first_year=(\"year\", \"min\"),\n",
    "        last_year=(\"year\", \"max\"),\n",
    "        observations=(\"year\", \"size\"),\n",
    "        missing_outcome=(\"renewable_index\", lambda x: x.isna().sum()),\n",
    "    )\n",
    "    .sort_index()\n",
    ")\n",
    "\n",
    "print(\"Any duplicate state and year rows:\", panel.duplicated([\"state\", \"year\"]).any())\n",
    "print(\"Treatment rows outside Colorado:\", int(panel.loc[panel[\"state\"] != treated, \"treatment\"].sum()))\n",
    "print(\"Colorado treatment start:\", panel.loc[panel[\"treatment\"].eq(1), \"year\"].min())\n",
    "\n",
    "coverage.head(10)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a7087f8",
   "metadata": {},
   "source": [
    "## 3. Donor screening\n",
    "\n",
    "All donors are eligible in this simulated example. Pre treatment correlation is included as a descriptive diagnostic only.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c247d9c6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-03T17:23:13.163829Z",
     "iopub.status.busy": "2026-09-03T17:23:13.163720Z",
     "iopub.status.idle": "2026-09-03T17:23:13.175819Z",
     "shell.execute_reply": "2026-09-03T17:23:13.175032Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>eligible</th>\n",
       "      <th>reason</th>\n",
       "      <th>pre_treatment_correlation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Idaho</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No simulated contamination or treatment overlap</td>\n",
       "      <td>0.982478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Indiana</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No simulated contamination or treatment overlap</td>\n",
       "      <td>0.959200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Oregon</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No simulated contamination or treatment overlap</td>\n",
       "      <td>0.953109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Nevada</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No simulated contamination or treatment overlap</td>\n",
       "      <td>0.939629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Connecticut</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No simulated contamination or treatment overlap</td>\n",
       "      <td>0.939381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>North Carolina</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No simulated contamination or treatment overlap</td>\n",
       "      <td>0.939087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No simulated contamination or treatment overlap</td>\n",
       "      <td>0.934158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Iowa</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No simulated contamination or treatment overlap</td>\n",
       "      <td>0.931859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Utah</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No simulated contamination or treatment overlap</td>\n",
       "      <td>0.928388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Maine</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No simulated contamination or treatment overlap</td>\n",
       "      <td>0.920893</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            state  ... pre_treatment_correlation\n",
       "0           Idaho  ...                  0.982478\n",
       "1         Indiana  ...                  0.959200\n",
       "2          Oregon  ...                  0.953109\n",
       "3          Nevada  ...                  0.939629\n",
       "4     Connecticut  ...                  0.939381\n",
       "5  North Carolina  ...                  0.939087\n",
       "6         Arizona  ...                  0.934158\n",
       "7            Iowa  ...                  0.931859\n",
       "8            Utah  ...                  0.928388\n",
       "9           Maine  ...                  0.920893\n",
       "\n",
       "[10 rows x 4 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corrs = {}\n",
    "\n",
    "for j, state in enumerate(donors):\n",
    "    corrs[state] = float(\n",
    "        np.corrcoef(\n",
    "            treated_y[pre_mask],\n",
    "            Yd[pre_mask, j],\n",
    "        )[0, 1]\n",
    "    )\n",
    "\n",
    "donor_screening = (\n",
    "    pd.DataFrame(\n",
    "        {\n",
    "            \"state\": donors,\n",
    "            \"eligible\": \"Yes\",\n",
    "            \"reason\": \"No simulated contamination or treatment overlap\",\n",
    "            \"pre_treatment_correlation\": [corrs[s] for s in donors],\n",
    "        }\n",
    "    )\n",
    "    .sort_values(\"pre_treatment_correlation\", ascending=False)\n",
    "    .reset_index(drop=True)\n",
    ")\n",
    "\n",
    "donor_screening.head(10)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56d3aa32",
   "metadata": {},
   "source": [
    "## 4. Fit classic synthetic control weights\n",
    "\n",
    "The optimizer uses nonnegative weights that sum to one. It minimizes scaled pre treatment outcome and predictor differences.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c0d121d4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-03T17:23:13.177056Z",
     "iopub.status.busy": "2026-09-03T17:23:13.176944Z",
     "iopub.status.idle": "2026-09-03T17:23:13.205585Z",
     "shell.execute_reply": "2026-09-03T17:23:13.205038Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Weight sum: 1.0\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>weight</th>\n",
       "      <th>weight_pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Idaho</td>\n",
       "      <td>0.393600</td>\n",
       "      <td>39.360012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Iowa</td>\n",
       "      <td>0.129475</td>\n",
       "      <td>12.947513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Oregon</td>\n",
       "      <td>0.111070</td>\n",
       "      <td>11.106961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>New Mexico</td>\n",
       "      <td>0.105778</td>\n",
       "      <td>10.577815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>0.079843</td>\n",
       "      <td>7.984346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Indiana</td>\n",
       "      <td>0.074959</td>\n",
       "      <td>7.495932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Connecticut</td>\n",
       "      <td>0.061880</td>\n",
       "      <td>6.187957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>0.011867</td>\n",
       "      <td>1.186683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Mississippi</td>\n",
       "      <td>0.011551</td>\n",
       "      <td>1.155108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>0.010050</td>\n",
       "      <td>1.005018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         state    weight  weight_pct\n",
       "0        Idaho  0.393600   39.360012\n",
       "1         Iowa  0.129475   12.947513\n",
       "2       Oregon  0.111070   11.106961\n",
       "3   New Mexico  0.105778   10.577815\n",
       "4    Wisconsin  0.079843    7.984346\n",
       "5      Indiana  0.074959    7.495932\n",
       "6  Connecticut  0.061880    6.187957\n",
       "7      Arizona  0.011867    1.186683\n",
       "8  Mississippi  0.011551    1.155108\n",
       "9     Virginia  0.010050    1.005018"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def fit_weights(X0, X1):\n",
    "    combined = np.column_stack([X1, X0])\n",
    "    sd = combined.std(axis=1, ddof=1)\n",
    "    sd[sd < 1e-12] = 1.0\n",
    "\n",
    "    X0s = X0 / sd[:, None]\n",
    "    X1s = X1 / sd\n",
    "\n",
    "    n = X0.shape[1]\n",
    "\n",
    "    def objective(w):\n",
    "        residual = X1s - X0s @ w\n",
    "        return float(residual @ residual)\n",
    "\n",
    "    result = minimize(\n",
    "        objective,\n",
    "        np.ones(n) / n,\n",
    "        method=\"SLSQP\",\n",
    "        bounds=[(0.0, 1.0)] * n,\n",
    "        constraints={\"type\": \"eq\", \"fun\": lambda w: w.sum() - 1.0},\n",
    "        options={\"maxiter\": 5000, \"ftol\": 1e-10},\n",
    "    )\n",
    "\n",
    "    if not result.success:\n",
    "        raise RuntimeError(result.message)\n",
    "\n",
    "    return result.x\n",
    "\n",
    "\n",
    "def fit_unit(unit_idx, pre_start_year=2005, use_predictors=True, exclude_units=None):\n",
    "    exclude_units = set(exclude_units or [])\n",
    "\n",
    "    donor_idx = [\n",
    "        j for j, unit in enumerate(units)\n",
    "        if j != unit_idx and unit not in exclude_units\n",
    "    ]\n",
    "\n",
    "    pmask = (years >= pre_start_year) & (years < policy_year)\n",
    "\n",
    "    X0 = Yall[pmask][:, donor_idx]\n",
    "    X1 = Yall[pmask, unit_idx]\n",
    "\n",
    "    if use_predictors:\n",
    "        X0 = np.vstack([X0, all_preds.iloc[donor_idx].T.values])\n",
    "        X1 = np.concatenate([X1, all_preds.iloc[unit_idx].values])\n",
    "\n",
    "    weights = fit_weights(X0, X1)\n",
    "    synthetic = Yall[:, donor_idx] @ weights\n",
    "    gap = Yall[:, unit_idx] - synthetic\n",
    "\n",
    "    pre_rmse = float(np.sqrt(np.mean(gap[pmask] ** 2)))\n",
    "    post_rmse = float(np.sqrt(np.mean(gap[post_mask] ** 2)))\n",
    "\n",
    "    return {\n",
    "        \"unit\": units[unit_idx],\n",
    "        \"donor_idx\": donor_idx,\n",
    "        \"weights\": weights,\n",
    "        \"synthetic\": synthetic,\n",
    "        \"gap\": gap,\n",
    "        \"pre_rmse\": pre_rmse,\n",
    "        \"post_rmse\": post_rmse,\n",
    "        \"ratio\": post_rmse / pre_rmse,\n",
    "        \"avg_effect\": float(np.mean(gap[post_mask])),\n",
    "    }\n",
    "\n",
    "\n",
    "headline = fit_unit(0)\n",
    "\n",
    "weight_table = (\n",
    "    pd.Series(\n",
    "        headline[\"weights\"],\n",
    "        index=[units[j] for j in headline[\"donor_idx\"]],\n",
    "        name=\"weight\",\n",
    "    )\n",
    "    .sort_values(ascending=False)\n",
    "    .rename_axis(\"state\")\n",
    "    .reset_index()\n",
    ")\n",
    "\n",
    "weight_table[\"weight_pct\"] = 100 * weight_table[\"weight\"]\n",
    "\n",
    "print(\"Weight sum:\", round(weight_table[\"weight\"].sum(), 10))\n",
    "weight_table.head(10)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb590e61",
   "metadata": {},
   "source": [
    "## 5. Pre treatment fit and actual versus synthetic path\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7c633005",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-03T17:23:13.206733Z",
     "iopub.status.busy": "2026-09-03T17:23:13.206623Z",
     "iopub.status.idle": "2026-09-03T17:23:13.487290Z",
     "shell.execute_reply": "2026-09-03T17:23:13.486811Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre treatment RMSE: 0.396\n",
      "Post treatment RMSE: 10.573\n",
      "Average post treatment effect: 9.641\n",
      "Post to pre RMSPE ratio: 26.719\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "actual = Yall[:, 0]\n",
    "synthetic = headline[\"synthetic\"]\n",
    "gap = headline[\"gap\"]\n",
    "\n",
    "print(\"Pre treatment RMSE:\", round(headline[\"pre_rmse\"], 3))\n",
    "print(\"Post treatment RMSE:\", round(headline[\"post_rmse\"], 3))\n",
    "print(\"Average post treatment effect:\", round(headline[\"avg_effect\"], 3))\n",
    "print(\"Post to pre RMSPE ratio:\", round(headline[\"ratio\"], 3))\n",
    "\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(years, actual, marker=\"o\", label=\"Colorado actual\")\n",
    "plt.plot(years, synthetic, marker=\"o\", label=\"Synthetic Colorado\")\n",
    "plt.axvline(policy_year, linestyle=\":\", label=\"Policy starts\")\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Renewable generation index\")\n",
    "plt.title(\"Actual and synthetic outcome\")\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.savefig(OUTPUT_DIR / \"actual_vs_synthetic.png\", dpi=160)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f866a94b",
   "metadata": {},
   "source": [
    "## 6. Policy effect over time\n",
    "\n",
    "The estimated effect is actual outcome minus synthetic outcome.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "be411d9c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-03T17:23:13.489266Z",
     "iopub.status.busy": "2026-09-03T17:23:13.489146Z",
     "iopub.status.idle": "2026-09-03T17:23:13.711050Z",
     "shell.execute_reply": "2026-09-03T17:23:13.710585Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>estimated_effect</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2018</td>\n",
       "      <td>3.428229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2019</td>\n",
       "      <td>5.185817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2020</td>\n",
       "      <td>6.955035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2021</td>\n",
       "      <td>10.293815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2022</td>\n",
       "      <td>11.513530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2023</td>\n",
       "      <td>13.695892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2024</td>\n",
       "      <td>16.411805</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year  estimated_effect\n",
       "13  2018          3.428229\n",
       "14  2019          5.185817\n",
       "15  2020          6.955035\n",
       "16  2021         10.293815\n",
       "17  2022         11.513530\n",
       "18  2023         13.695892\n",
       "19  2024         16.411805"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "effect_path = pd.DataFrame(\n",
    "    {\n",
    "        \"year\": years,\n",
    "        \"actual\": actual,\n",
    "        \"synthetic\": synthetic,\n",
    "        \"estimated_effect\": gap,\n",
    "        \"period\": np.where(years < policy_year, \"Pre treatment\", \"Post treatment\"),\n",
    "    }\n",
    ")\n",
    "\n",
    "post_effects = effect_path.loc[\n",
    "    effect_path[\"year\"] >= policy_year,\n",
    "    [\"year\", \"estimated_effect\"],\n",
    "].copy()\n",
    "\n",
    "display(post_effects)\n",
    "\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(years, gap, marker=\"o\", label=\"Estimated effect\")\n",
    "plt.axhline(0)\n",
    "plt.axvline(policy_year, linestyle=\":\", label=\"Policy starts\")\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Actual minus synthetic\")\n",
    "plt.title(\"Estimated policy effect over time\")\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.savefig(OUTPUT_DIR / \"effect_gap.png\", dpi=160)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa7c863e",
   "metadata": {},
   "source": [
    "## 7. Donor weights\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "80613062",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-03T17:23:13.712531Z",
     "iopub.status.busy": "2026-09-03T17:23:13.712244Z",
     "iopub.status.idle": "2026-09-03T17:23:13.905257Z",
     "shell.execute_reply": "2026-09-03T17:23:13.904766Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>weight</th>\n",
       "      <th>weight_pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Idaho</td>\n",
       "      <td>0.393600</td>\n",
       "      <td>39.360012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Iowa</td>\n",
       "      <td>0.129475</td>\n",
       "      <td>12.947513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Oregon</td>\n",
       "      <td>0.111070</td>\n",
       "      <td>11.106961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>New Mexico</td>\n",
       "      <td>0.105778</td>\n",
       "      <td>10.577815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>0.079843</td>\n",
       "      <td>7.984346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Indiana</td>\n",
       "      <td>0.074959</td>\n",
       "      <td>7.495932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Connecticut</td>\n",
       "      <td>0.061880</td>\n",
       "      <td>6.187957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>0.011867</td>\n",
       "      <td>1.186683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Mississippi</td>\n",
       "      <td>0.011551</td>\n",
       "      <td>1.155108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>0.010050</td>\n",
       "      <td>1.005018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>North Carolina</td>\n",
       "      <td>0.009927</td>\n",
       "      <td>0.992655</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             state    weight  weight_pct\n",
       "0            Idaho  0.393600   39.360012\n",
       "1             Iowa  0.129475   12.947513\n",
       "2           Oregon  0.111070   11.106961\n",
       "3       New Mexico  0.105778   10.577815\n",
       "4        Wisconsin  0.079843    7.984346\n",
       "5          Indiana  0.074959    7.495932\n",
       "6      Connecticut  0.061880    6.187957\n",
       "7          Arizona  0.011867    1.186683\n",
       "8      Mississippi  0.011551    1.155108\n",
       "9         Virginia  0.010050    1.005018\n",
       "10  North Carolina  0.009927    0.992655"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 900x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nonzero_weights = weight_table.loc[weight_table[\"weight\"] > 0.001].copy()\n",
    "display(nonzero_weights)\n",
    "\n",
    "plt.figure(figsize=(9, 5))\n",
    "plot_weights = nonzero_weights.sort_values(\"weight\")\n",
    "plt.barh(plot_weights[\"state\"], plot_weights[\"weight\"])\n",
    "plt.xlabel(\"Synthetic control weight\")\n",
    "plt.ylabel(\"Donor state\")\n",
    "plt.title(\"Largest donor weights\")\n",
    "plt.tight_layout()\n",
    "plt.savefig(OUTPUT_DIR / \"donor_weights.png\", dpi=160)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5e27ca0",
   "metadata": {},
   "source": [
    "## 8. Predictor balance\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6a87969b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-03T17:23:13.906471Z",
     "iopub.status.busy": "2026-09-03T17:23:13.906352Z",
     "iopub.status.idle": "2026-09-03T17:23:13.916714Z",
     "shell.execute_reply": "2026-09-03T17:23:13.916258Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>predictor</th>\n",
       "      <th>treated</th>\n",
       "      <th>synthetic</th>\n",
       "      <th>raw_donor_mean</th>\n",
       "      <th>absolute_treated_synthetic_gap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Income Index</td>\n",
       "      <td>106.294655</td>\n",
       "      <td>106.218767</td>\n",
       "      <td>105.765464</td>\n",
       "      <td>0.075888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Industrial Share</td>\n",
       "      <td>21.755629</td>\n",
       "      <td>21.762395</td>\n",
       "      <td>19.393591</td>\n",
       "      <td>0.006766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Electricity Price Index</td>\n",
       "      <td>105.351570</td>\n",
       "      <td>105.322184</td>\n",
       "      <td>102.397622</td>\n",
       "      <td>0.029386</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 predictor  ...  absolute_treated_synthetic_gap\n",
       "0             Income Index  ...                        0.075888\n",
       "1         Industrial Share  ...                        0.006766\n",
       "2  Electricity Price Index  ...                        0.029386\n",
       "\n",
       "[3 rows x 5 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "balance_rows = []\n",
    "\n",
    "headline_donors = [units[j] for j in headline[\"donor_idx\"]]\n",
    "\n",
    "for predictor in pred_names:\n",
    "    synthetic_value = float(\n",
    "        np.dot(\n",
    "            headline[\"weights\"],\n",
    "            all_preds.loc[headline_donors, predictor].values,\n",
    "        )\n",
    "    )\n",
    "\n",
    "    balance_rows.append(\n",
    "        {\n",
    "            \"predictor\": predictor.replace(\"_\", \" \").title(),\n",
    "            \"treated\": float(all_preds.loc[treated, predictor]),\n",
    "            \"synthetic\": synthetic_value,\n",
    "            \"raw_donor_mean\": float(all_preds.loc[donors, predictor].mean()),\n",
    "        }\n",
    "    )\n",
    "\n",
    "balance_table = pd.DataFrame(balance_rows)\n",
    "balance_table[\"absolute_treated_synthetic_gap\"] = (\n",
    "    balance_table[\"treated\"] - balance_table[\"synthetic\"]\n",
    ").abs()\n",
    "\n",
    "balance_table\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a566202a",
   "metadata": {},
   "source": [
    "## 9. In space placebo tests\n",
    "\n",
    "Each state is treated as if it had received the intervention. The post to pre RMSPE ratio is then compared with Colorado.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "740689af",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-03T17:23:13.917857Z",
     "iopub.status.busy": "2026-09-03T17:23:13.917763Z",
     "iopub.status.idle": "2026-09-03T17:23:14.088279Z",
     "shell.execute_reply": "2026-09-03T17:23:14.087686Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Colorado placebo rank: 1 of 22\n",
      "Rank based permutation value: 0.045\n",
      "Smallest possible rank value: 0.045\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>pre_treatment_rmse</th>\n",
       "      <th>post_treatment_rmse</th>\n",
       "      <th>post_to_pre_rmspe_ratio</th>\n",
       "      <th>treated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Colorado</td>\n",
       "      <td>0.395704</td>\n",
       "      <td>10.572652</td>\n",
       "      <td>26.718597</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Iowa</td>\n",
       "      <td>0.990365</td>\n",
       "      <td>5.102584</td>\n",
       "      <td>5.152225</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>North Carolina</td>\n",
       "      <td>1.024519</td>\n",
       "      <td>4.179814</td>\n",
       "      <td>4.079780</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>1.375797</td>\n",
       "      <td>5.451769</td>\n",
       "      <td>3.962626</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Idaho</td>\n",
       "      <td>0.666570</td>\n",
       "      <td>2.567252</td>\n",
       "      <td>3.851435</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>0.841496</td>\n",
       "      <td>3.128734</td>\n",
       "      <td>3.718062</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Connecticut</td>\n",
       "      <td>1.278846</td>\n",
       "      <td>3.890642</td>\n",
       "      <td>3.042307</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>New Mexico</td>\n",
       "      <td>0.891189</td>\n",
       "      <td>2.551601</td>\n",
       "      <td>2.863144</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Oregon</td>\n",
       "      <td>1.830072</td>\n",
       "      <td>4.436974</td>\n",
       "      <td>2.424481</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Missouri</td>\n",
       "      <td>1.609874</td>\n",
       "      <td>3.799091</td>\n",
       "      <td>2.359868</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            state  pre_treatment_rmse  ...  post_to_pre_rmspe_ratio  treated\n",
       "0        Colorado            0.395704  ...                26.718597     True\n",
       "1            Iowa            0.990365  ...                 5.152225    False\n",
       "2  North Carolina            1.024519  ...                 4.079780    False\n",
       "3        Virginia            1.375797  ...                 3.962626    False\n",
       "4           Idaho            0.666570  ...                 3.851435    False\n",
       "5       Wisconsin            0.841496  ...                 3.718062    False\n",
       "6     Connecticut            1.278846  ...                 3.042307    False\n",
       "7      New Mexico            0.891189  ...                 2.863144    False\n",
       "8          Oregon            1.830072  ...                 2.424481    False\n",
       "9        Missouri            1.609874  ...                 2.359868    False\n",
       "\n",
       "[10 rows x 5 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "placebo_results = [fit_unit(i) for i in range(len(units))]\n",
    "\n",
    "placebo_table = pd.DataFrame(\n",
    "    {\n",
    "        \"state\": [r[\"unit\"] for r in placebo_results],\n",
    "        \"pre_treatment_rmse\": [r[\"pre_rmse\"] for r in placebo_results],\n",
    "        \"post_treatment_rmse\": [r[\"post_rmse\"] for r in placebo_results],\n",
    "        \"post_to_pre_rmspe_ratio\": [r[\"ratio\"] for r in placebo_results],\n",
    "    }\n",
    ")\n",
    "\n",
    "placebo_table[\"treated\"] = placebo_table[\"state\"].eq(treated)\n",
    "\n",
    "placebo_table = placebo_table.sort_values(\n",
    "    \"post_to_pre_rmspe_ratio\",\n",
    "    ascending=False,\n",
    ").reset_index(drop=True)\n",
    "\n",
    "placebo_p = float(\n",
    "    (\n",
    "        placebo_table[\"post_to_pre_rmspe_ratio\"]\n",
    "        >= headline[\"ratio\"] - 1e-12\n",
    "    ).mean()\n",
    ")\n",
    "\n",
    "colorado_rank = int(\n",
    "    placebo_table.index[placebo_table[\"state\"].eq(treated)][0]\n",
    ") + 1\n",
    "\n",
    "print(\"Colorado placebo rank:\", colorado_rank, \"of\", len(placebo_table))\n",
    "print(\"Rank based permutation value:\", round(placebo_p, 3))\n",
    "print(\"Smallest possible rank value:\", round(1 / len(placebo_table), 3))\n",
    "\n",
    "placebo_table.head(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b9a0ea6d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-03T17:23:14.089610Z",
     "iopub.status.busy": "2026-09-03T17:23:14.089480Z",
     "iopub.status.idle": "2026-09-03T17:23:14.370359Z",
     "shell.execute_reply": "2026-09-03T17:23:14.369887Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 900x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_placebos = placebo_table.sort_values(\"post_to_pre_rmspe_ratio\")\n",
    "\n",
    "plt.figure(figsize=(9, 7))\n",
    "plt.barh(\n",
    "    plot_placebos[\"state\"],\n",
    "    plot_placebos[\"post_to_pre_rmspe_ratio\"],\n",
    ")\n",
    "plt.xlabel(\"Post to pre RMSPE ratio\")\n",
    "plt.ylabel(\"State\")\n",
    "plt.title(\"In space placebo comparison\")\n",
    "plt.tight_layout()\n",
    "plt.savefig(OUTPUT_DIR / \"placebo_ratios.png\", dpi=160)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e346577",
   "metadata": {},
   "source": [
    "## 10. Placebo gap paths\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7923f9ac",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-03T17:23:14.372255Z",
     "iopub.status.busy": "2026-09-03T17:23:14.372018Z",
     "iopub.status.idle": "2026-09-03T17:23:14.634532Z",
     "shell.execute_reply": "2026-09-03T17:23:14.633934Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "for result in placebo_results:\n",
    "    if result[\"unit\"] != treated:\n",
    "        plt.plot(years, result[\"gap\"], alpha=0.35, linewidth=1)\n",
    "\n",
    "plt.plot(years, headline[\"gap\"], linewidth=3, label=\"Colorado\")\n",
    "plt.axhline(0)\n",
    "plt.axvline(policy_year, linestyle=\":\", label=\"Policy starts\")\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Actual minus synthetic\")\n",
    "plt.title(\"Colorado gap compared with placebo gaps\")\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.savefig(OUTPUT_DIR / \"placebo_gap_paths.png\", dpi=160)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ca87e62",
   "metadata": {},
   "source": [
    "## 11. Robustness and sensitivity checks\n",
    "\n",
    "These specifications alter the pre period, predictor use, and high weight donors.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ce58b9a3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-03T17:23:14.637906Z",
     "iopub.status.busy": "2026-09-03T17:23:14.637781Z",
     "iopub.status.idle": "2026-09-03T17:23:14.737388Z",
     "shell.execute_reply": "2026-09-03T17:23:14.736846Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>specification</th>\n",
       "      <th>pre_treatment_rmse</th>\n",
       "      <th>average_post_treatment_effect</th>\n",
       "      <th>post_to_pre_rmspe_ratio</th>\n",
       "      <th>same_effect_direction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Headline model</td>\n",
       "      <td>0.395704</td>\n",
       "      <td>9.640589</td>\n",
       "      <td>26.718597</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Start pre period in 2008</td>\n",
       "      <td>0.429040</td>\n",
       "      <td>9.840102</td>\n",
       "      <td>25.080144</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Outcome history only</td>\n",
       "      <td>0.344615</td>\n",
       "      <td>10.395837</td>\n",
       "      <td>33.104483</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Leave out Idaho</td>\n",
       "      <td>0.490276</td>\n",
       "      <td>8.645492</td>\n",
       "      <td>19.213809</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Leave out Iowa</td>\n",
       "      <td>0.417411</td>\n",
       "      <td>10.326195</td>\n",
       "      <td>27.202671</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Leave out Oregon</td>\n",
       "      <td>0.403334</td>\n",
       "      <td>8.879229</td>\n",
       "      <td>24.138216</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              specification  ...  same_effect_direction\n",
       "0            Headline model  ...                   True\n",
       "1  Start pre period in 2008  ...                   True\n",
       "2      Outcome history only  ...                   True\n",
       "3           Leave out Idaho  ...                   True\n",
       "4            Leave out Iowa  ...                   True\n",
       "5          Leave out Oregon  ...                   True\n",
       "\n",
       "[6 rows x 5 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def variant(name, **kwargs):\n",
    "    result = fit_unit(0, **kwargs)\n",
    "    return {\n",
    "        \"specification\": name,\n",
    "        \"pre_treatment_rmse\": result[\"pre_rmse\"],\n",
    "        \"average_post_treatment_effect\": result[\"avg_effect\"],\n",
    "        \"post_to_pre_rmspe_ratio\": result[\"ratio\"],\n",
    "    }\n",
    "\n",
    "robustness_rows = [\n",
    "    variant(\"Headline model\"),\n",
    "    variant(\"Start pre period in 2008\", pre_start_year=2008),\n",
    "    variant(\"Outcome history only\", use_predictors=False),\n",
    "]\n",
    "\n",
    "for donor in weight_table.head(3)[\"state\"]:\n",
    "    robustness_rows.append(\n",
    "        variant(f\"Leave out {donor}\", exclude_units=[donor])\n",
    "    )\n",
    "\n",
    "robustness_table = pd.DataFrame(robustness_rows)\n",
    "\n",
    "headline_sign = np.sign(headline[\"avg_effect\"])\n",
    "robustness_table[\"same_effect_direction\"] = (\n",
    "    np.sign(robustness_table[\"average_post_treatment_effect\"]) == headline_sign\n",
    ")\n",
    "\n",
    "robustness_table\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0d9fe5f",
   "metadata": {},
   "source": [
    "## 12. Interactive Plotly charts\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6fb33027",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-03T17:23:14.738619Z",
     "iopub.status.busy": "2026-09-03T17:23:14.738509Z",
     "iopub.status.idle": "2026-09-03T17:23:15.792388Z",
     "shell.execute_reply": "2026-09-03T17:23:15.791821Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "mode": "lines+markers",
         "name": "Colorado actual",
         "type": "scatter",
         "x": {
          "bdata": "1QfWB9cH2AfZB9oH2wfcB90H3gffB+AH4QfiB+MH5AflB+YH5wfoBw==",
          "dtype": "i2"
         },
         "y": {
          "bdata": "Tmrekm+GVUD6hfexxgBWQGIqrP9OhFZAUmcjTPLyVkAKxBs3zQRXQIZ6xqtkIFdA6R9NhZ/7VkCF8cHjdI1XQBX68wnLWVdAWXvKqjE4V0DPHyB6B3hXQKmapM8GOldAUlGNJGLCV0Cy75/AIatYQGcR+khGMVlAQkcZ26ICWkDEZnxxtw1bQD7aRunG2VtAhNOKkpyiXEAFeBszLaldQA==",
          "dtype": "f8"
         }
        },
        {
         "mode": "lines+markers",
         "name": "Synthetic Colorado",
         "type": "scatter",
         "x": {
          "bdata": "1QfWB9cH2AfZB9oH2wfcB90H3gffB+AH4QfiB+MH5AflB+YH5wfoBw==",
          "dtype": "i2"
         },
         "y": {
          "bdata": "Y/zf3BKXVUBLiSobaAFWQCeJx/mjfFZAcitWyw7IVkDfuyTUSf1WQLkxa3WnKldAd9SAYfkZV0DuADURfm5XQFUaRdvob1dAGpnnyXcvV0AkulE/kWRXQInWNVoMcFdA1/mcOlGyV0A9jfOkuc9XQLcneNth5VdAG3YNkINFWEA/LyaT6XpYQEzQgDrp+FhAwqJ2ExM2WUAsglEv0o5ZQA==",
          "dtype": "f8"
         }
        }
       ],
       "layout": {
        "annotations": [
         {
          "showarrow": false,
          "text": "Policy starts",
          "x": 2018,
          "xanchor": "left",
          "xref": "x",
          "y": 1,
          "yanchor": "top",
          "yref": "y domain"
         }
        ],
        "shapes": [
         {
          "line": {
           "dash": "dot"
          },
          "type": "line",
          "x0": 2018,
          "x1": 2018,
          "xref": "x",
          "y0": 0,
          "y1": 1,
          "yref": "y domain"
         }
        ],
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0.0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1.0,
              "#f0f921"
             ]
            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0.0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1.0,
              "#f0f921"
             ]
            ],
            "type": "heatmap"
           }
          ],
          "histogram": [
           {
            "marker": {
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "histogram"
           }
          ],
          "histogram2d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0.0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1.0,
              "#f0f921"
             ]
            ],
            "type": "histogram2d"
           }
          ],
          "histogram2dcontour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0.0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1.0,
              "#f0f921"
             ]
            ],
            "type": "histogram2dcontour"
           }
          ],
          "mesh3d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "mesh3d"
           }
          ],
          "parcoords": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "parcoords"
           }
          ],
          "pie": [
           {
            "automargin": true,
            "type": "pie"
           }
          ],
          "scatter": [
           {
            "fillpattern": {
             "fillmode": "overlay",
             "size": 10,
             "solidity": 0.2
            },
            "type": "scatter"
           }
          ],
          "scatter3d": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter3d"
           }
          ],
          "scattercarpet": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattercarpet"
           }
          ],
          "scattergeo": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergeo"
           }
          ],
          "scattergl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergl"
           }
          ],
          "scattermap": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattermap"
           }
          ],
          "scattermapbox": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattermapbox"
           }
          ],
          "scatterpolar": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolar"
           }
          ],
          "scatterpolargl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolargl"
           }
          ],
          "scatterternary": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterternary"
           }
          ],
          "surface": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0.0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1.0,
              "#f0f921"
             ]
            ],
            "type": "surface"
           }
          ],
          "table": [
           {
            "cells": {
             "fill": {
              "color": "#EBF0F8"
             },
             "line": {
              "color": "white"
             }
            },
            "header": {
             "fill": {
              "color": "#C8D4E3"
             },
             "line": {
              "color": "white"
             }
            },
            "type": "table"
           }
          ]
         },
         "layout": {
          "annotationdefaults": {
           "arrowcolor": "#2a3f5f",
           "arrowhead": 0,
           "arrowwidth": 1
          },
          "autotypenumbers": "strict",
          "coloraxis": {
           "colorbar": {
            "outlinewidth": 0,
            "ticks": ""
           }
          },
          "colorscale": {
           "diverging": [
            [
             0,
             "#8e0152"
            ],
            [
             0.1,
             "#c51b7d"
            ],
            [
             0.2,
             "#de77ae"
            ],
            [
             0.3,
             "#f1b6da"
            ],
            [
             0.4,
             "#fde0ef"
            ],
            [
             0.5,
             "#f7f7f7"
            ],
            [
             0.6,
             "#e6f5d0"
            ],
            [
             0.7,
             "#b8e186"
            ],
            [
             0.8,
             "#7fbc41"
            ],
            [
             0.9,
             "#4d9221"
            ],
            [
             1,
             "#276419"
            ]
           ],
           "sequential": [
            [
             0.0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1.0,
             "#f0f921"
            ]
           ],
           "sequentialminus": [
            [
             0.0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1.0,
             "#f0f921"
            ]
           ]
          },
          "colorway": [
           "#636efa",
           "#EF553B",
           "#00cc96",
           "#ab63fa",
           "#FFA15A",
           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "Actual outcome and synthetic counterfactual"
        },
        "xaxis": {
         "title": {
          "text": "Year"
         }
        },
        "yaxis": {
         "title": {
          "text": "Renewable generation index"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if PLOTLY_AVAILABLE:\n",
    "    fig = go.Figure()\n",
    "\n",
    "    fig.add_trace(\n",
    "        go.Scatter(\n",
    "            x=years,\n",
    "            y=actual,\n",
    "            mode=\"lines+markers\",\n",
    "            name=\"Colorado actual\",\n",
    "        )\n",
    "    )\n",
    "\n",
    "    fig.add_trace(\n",
    "        go.Scatter(\n",
    "            x=years,\n",
    "            y=synthetic,\n",
    "            mode=\"lines+markers\",\n",
    "            name=\"Synthetic Colorado\",\n",
    "        )\n",
    "    )\n",
    "\n",
    "    fig.add_vline(\n",
    "        x=policy_year,\n",
    "        line_dash=\"dot\",\n",
    "        annotation_text=\"Policy starts\",\n",
    "    )\n",
    "\n",
    "    fig.update_layout(\n",
    "        title=\"Actual outcome and synthetic counterfactual\",\n",
    "        xaxis_title=\"Year\",\n",
    "        yaxis_title=\"Renewable generation index\",\n",
    "    )\n",
    "\n",
    "    fig.show()\n",
    "else:\n",
    "    print(\"Plotly is not installed. Static charts above are complete.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "06659ae8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-03T17:23:15.793845Z",
     "iopub.status.busy": "2026-09-03T17:23:15.793730Z",
     "iopub.status.idle": "2026-09-03T17:23:15.805131Z",
     "shell.execute_reply": "2026-09-03T17:23:15.804637Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "mode": "lines+markers",
         "name": "Estimated effect",
         "type": "scatter",
         "x": {
          "bdata": "1QfWB9cH2AfZB9oH2wfcB90H3gffB+AH4QfiB+MH5AflB+YH5wfoBw==",
          "dtype": "i2"
         },
         "y": {
          "bdata": "ABWSAUqj0L8AIGpgJi2EvwDshJIXrL4/APCdZsBx5T8ArCDciw2+PwBmbkmThcS/AI60M9xZ3r8Al/CM0vbePwBAIFHRHda/AH7ExcFzwT8Aq2XOOnbTPwDwnUjFAuu/AHtX8OkQ0D+gTox1A20LQACbHthGvhRAcBK9sPTRG0AovLHybpYkQJBPMHbtBidAEIah+EtkK0Bk1ycPbGkwQA==",
          "dtype": "f8"
         }
        }
       ],
       "layout": {
        "annotations": [
         {
          "showarrow": false,
          "text": "Policy starts",
          "x": 2018,
          "xanchor": "left",
          "xref": "x",
          "y": 1,
          "yanchor": "top",
          "yref": "y domain"
         }
        ],
        "shapes": [
         {
          "type": "line",
          "x0": 0,
          "x1": 1,
          "xref": "x domain",
          "y0": 0,
          "y1": 0,
          "yref": "y"
         },
         {
          "line": {
           "dash": "dot"
          },
          "type": "line",
          "x0": 2018,
          "x1": 2018,
          "xref": "x",
          "y0": 0,
          "y1": 1,
          "yref": "y domain"
         }
        ],
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0.0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1.0,
              "#f0f921"
             ]
            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0.0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1.0,
              "#f0f921"
             ]
            ],
            "type": "heatmap"
           }
          ],
          "histogram": [
           {
            "marker": {
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "histogram"
           }
          ],
          "histogram2d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0.0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1.0,
              "#f0f921"
             ]
            ],
            "type": "histogram2d"
           }
          ],
          "histogram2dcontour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0.0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1.0,
              "#f0f921"
             ]
            ],
            "type": "histogram2dcontour"
           }
          ],
          "mesh3d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "mesh3d"
           }
          ],
          "parcoords": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "parcoords"
           }
          ],
          "pie": [
           {
            "automargin": true,
            "type": "pie"
           }
          ],
          "scatter": [
           {
            "fillpattern": {
             "fillmode": "overlay",
             "size": 10,
             "solidity": 0.2
            },
            "type": "scatter"
           }
          ],
          "scatter3d": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter3d"
           }
          ],
          "scattercarpet": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattercarpet"
           }
          ],
          "scattergeo": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergeo"
           }
          ],
          "scattergl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergl"
           }
          ],
          "scattermap": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattermap"
           }
          ],
          "scattermapbox": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattermapbox"
           }
          ],
          "scatterpolar": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolar"
           }
          ],
          "scatterpolargl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolargl"
           }
          ],
          "scatterternary": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterternary"
           }
          ],
          "surface": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0.0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1.0,
              "#f0f921"
             ]
            ],
            "type": "surface"
           }
          ],
          "table": [
           {
            "cells": {
             "fill": {
              "color": "#EBF0F8"
             },
             "line": {
              "color": "white"
             }
            },
            "header": {
             "fill": {
              "color": "#C8D4E3"
             },
             "line": {
              "color": "white"
             }
            },
            "type": "table"
           }
          ]
         },
         "layout": {
          "annotationdefaults": {
           "arrowcolor": "#2a3f5f",
           "arrowhead": 0,
           "arrowwidth": 1
          },
          "autotypenumbers": "strict",
          "coloraxis": {
           "colorbar": {
            "outlinewidth": 0,
            "ticks": ""
           }
          },
          "colorscale": {
           "diverging": [
            [
             0,
             "#8e0152"
            ],
            [
             0.1,
             "#c51b7d"
            ],
            [
             0.2,
             "#de77ae"
            ],
            [
             0.3,
             "#f1b6da"
            ],
            [
             0.4,
             "#fde0ef"
            ],
            [
             0.5,
             "#f7f7f7"
            ],
            [
             0.6,
             "#e6f5d0"
            ],
            [
             0.7,
             "#b8e186"
            ],
            [
             0.8,
             "#7fbc41"
            ],
            [
             0.9,
             "#4d9221"
            ],
            [
             1,
             "#276419"
            ]
           ],
           "sequential": [
            [
             0.0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1.0,
             "#f0f921"
            ]
           ],
           "sequentialminus": [
            [
             0.0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1.0,
             "#f0f921"
            ]
           ]
          },
          "colorway": [
           "#636efa",
           "#EF553B",
           "#00cc96",
           "#ab63fa",
           "#FFA15A",
           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "Estimated policy effect over time"
        },
        "xaxis": {
         "title": {
          "text": "Year"
         }
        },
        "yaxis": {
         "title": {
          "text": "Actual minus synthetic"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if PLOTLY_AVAILABLE:\n",
    "    fig_gap = go.Figure()\n",
    "\n",
    "    fig_gap.add_trace(\n",
    "        go.Scatter(\n",
    "            x=years,\n",
    "            y=gap,\n",
    "            mode=\"lines+markers\",\n",
    "            name=\"Estimated effect\",\n",
    "        )\n",
    "    )\n",
    "\n",
    "    fig_gap.add_hline(y=0)\n",
    "    fig_gap.add_vline(\n",
    "        x=policy_year,\n",
    "        line_dash=\"dot\",\n",
    "        annotation_text=\"Policy starts\",\n",
    "    )\n",
    "\n",
    "    fig_gap.update_layout(\n",
    "        title=\"Estimated policy effect over time\",\n",
    "        xaxis_title=\"Year\",\n",
    "        yaxis_title=\"Actual minus synthetic\",\n",
    "    )\n",
    "\n",
    "    fig_gap.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f7dfec9",
   "metadata": {},
   "source": [
    "## 13. Animated donor buildup\n",
    "\n",
    "This animation shows how weighted donor contributions build the synthetic path.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0906177a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-03T17:23:15.806510Z",
     "iopub.status.busy": "2026-09-03T17:23:15.806183Z",
     "iopub.status.idle": "2026-09-03T17:23:15.823572Z",
     "shell.execute_reply": "2026-09-03T17:23:15.822962Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "mode": "lines",
         "name": "Colorado actual",
         "type": "scatter",
         "x": {
          "bdata": "1QfWB9cH2AfZB9oH2wfcB90H3gffB+AH4QfiB+MH5AflB+YH5wfoBw==",
          "dtype": "i2"
         },
         "y": {
          "bdata": "Tmrekm+GVUD6hfexxgBWQGIqrP9OhFZAUmcjTPLyVkAKxBs3zQRXQIZ6xqtkIFdA6R9NhZ/7VkCF8cHjdI1XQBX68wnLWVdAWXvKqjE4V0DPHyB6B3hXQKmapM8GOldAUlGNJGLCV0Cy75/AIatYQGcR+khGMVlAQkcZ26ICWkDEZnxxtw1bQD7aRunG2VtAhNOKkpyiXEAFeBszLaldQA==",
          "dtype": "f8"
         }
        },
        {
         "mode": "lines",
         "name": "Cumulative donor contribution",
         "type": "scatter",
         "x": {
          "bdata": "1QfWB9cH2AfZB9oH2wfcB90H3gffB+AH4QfiB+MH5AflB+YH5wfoBw==",
          "dtype": "i2"
         },
         "y": {
          "bdata": "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA==",
          "dtype": "f8"
         }
        }
       ],
       "frames": [
        {
         "data": [
          {
           "mode": "lines",
           "name": "Colorado actual",
           "type": "scatter",
           "x": {
            "bdata": "1QfWB9cH2AfZB9oH2wfcB90H3gffB+AH4QfiB+MH5AflB+YH5wfoBw==",
            "dtype": "i2"
           },
           "y": {
            "bdata": "Tmrekm+GVUD6hfexxgBWQGIqrP9OhFZAUmcjTPLyVkAKxBs3zQRXQIZ6xqtkIFdA6R9NhZ/7VkCF8cHjdI1XQBX68wnLWVdAWXvKqjE4V0DPHyB6B3hXQKmapM8GOldAUlGNJGLCV0Cy75/AIatYQGcR+khGMVlAQkcZ26ICWkDEZnxxtw1bQD7aRunG2VtAhNOKkpyiXEAFeBszLaldQA==",
            "dtype": "f8"
           }
          },
          {
           "mode": "lines",
           "name": "Cumulative donor contribution",
           "type": "scatter",
           "x": {
            "bdata": "1QfWB9cH2AfZB9oH2wfcB90H3gffB+AH4QfiB+MH5AflB+YH5wfoBw==",
            "dtype": "i2"
           },
           "y": {
            "bdata": "PEpFFJIlQUBqTa9uAIBBQMM1fLps7EFAdQ2l3zghQkClqyc18j5CQC7FGP5XdkJAAuDeg2JHQkBA1KJbbqJCQIvzbPFFwkJAjwqmPg6VQkCG/0c2XsdCQHnOpjrLrUJAQfylaVvvQkACHIoC+ANDQDzkkXIA2kJAoiA/xERLQ0BtJhOsNTFDQObt7hqmukNALNynjQiGQ0AovTFQds9DQA==",
            "dtype": "f8"
           }
          }
         ],
         "name": "1"
        },
        {
         "data": [
          {
           "mode": "lines",
           "name": "Colorado actual",
           "type": "scatter",
           "x": {
            "bdata": "1QfWB9cH2AfZB9oH2wfcB90H3gffB+AH4QfiB+MH5AflB+YH5wfoBw==",
            "dtype": "i2"
           },
           "y": {
            "bdata": "Tmrekm+GVUD6hfexxgBWQGIqrP9OhFZAUmcjTPLyVkAKxBs3zQRXQIZ6xqtkIFdA6R9NhZ/7VkCF8cHjdI1XQBX68wnLWVdAWXvKqjE4V0DPHyB6B3hXQKmapM8GOldAUlGNJGLCV0Cy75/AIatYQGcR+khGMVlAQkcZ26ICWkDEZnxxtw1bQD7aRunG2VtAhNOKkpyiXEAFeBszLaldQA==",
            "dtype": "f8"
           }
          },
          {
           "mode": "lines",
           "name": "Cumulative donor contribution",
           "type": "scatter",
           "x": {
            "bdata": "1QfWB9cH2AfZB9oH2wfcB90H3gffB+AH4QfiB+MH5AflB+YH5wfoBw==",
            "dtype": "i2"
           },
           "y": {
            "bdata": "RmSpJ5rsRkBPHE103XRHQCI3Yg36+0dAQMqwVF4zSEC9koFI+3BIQNaB7kLUokhArXW3aK+KSEBRyLS3evlIQBLtBq/iDUlAjlY6eQ68SEDY3t9iu/lIQPq1XG+T+khARZmH/AJlSUCWM3s7fH1JQAnjh18ybElAbQt09pHpSUDZugDaBQdKQIrNwu9Pl0pAdgPma/asSkBy22xyWydLQA==",
            "dtype": "f8"
           }
          }
         ],
         "name": "2"
        },
        {
         "data": [
          {
           "mode": "lines",
           "name": "Colorado actual",
           "type": "scatter",
           "x": {
            "bdata": "1QfWB9cH2AfZB9oH2wfcB90H3gffB+AH4QfiB+MH5AflB+YH5wfoBw==",
            "dtype": "i2"
           },
           "y": {
            "bdata": "Tmrekm+GVUD6hfexxgBWQGIqrP9OhFZAUmcjTPLyVkAKxBs3zQRXQIZ6xqtkIFdA6R9NhZ/7VkCF8cHjdI1XQBX68wnLWVdAWXvKqjE4V0DPHyB6B3hXQKmapM8GOldAUlGNJGLCV0Cy75/AIatYQGcR+khGMVlAQkcZ26ICWkDEZnxxtw1bQD7aRunG2VtAhNOKkpyiXEAFeBszLaldQA==",
            "dtype": "f8"
           }
          },
          {
           "mode": "lines",
           "name": "Cumulative donor contribution",
           "type": "scatter",
           "x": {
            "bdata": "1QfWB9cH2AfZB9oH2wfcB90H3gffB+AH4QfiB+MH5AflB+YH5wfoBw==",
            "dtype": "i2"
           },
           "y": {
            "bdata": "HqSSz8DOSkCKffZi3ldLQOJ92Ede9UtAarhKn39RTEBZpBfi65pMQAKWErMN5ExAtXKMLArFTECDuF0yHFNNQLYNb+z6bk1AVA14IDz+TEBwCndwklFNQFdrqAr7TU1AmFAVCsPOTUB+RELeJ95NQKtAgFNZ3E1ABOQJk8NXTkAm+8bqr4VOQHS3Z2FlOE9AbqAm9wxcT0CWJXLRMbpPQA==",
            "dtype": "f8"
           }
          }
         ],
         "name": "3"
        },
        {
         "data": [
          {
           "mode": "lines",
           "name": "Colorado actual",
           "type": "scatter",
           "x": {
            "bdata": "1QfWB9cH2AfZB9oH2wfcB90H3gffB+AH4QfiB+MH5AflB+YH5wfoBw==",
            "dtype": "i2"
           },
           "y": {
            "bdata": "Tmrekm+GVUD6hfexxgBWQGIqrP9OhFZAUmcjTPLyVkAKxBs3zQRXQIZ6xqtkIFdA6R9NhZ/7VkCF8cHjdI1XQBX68wnLWVdAWXvKqjE4V0DPHyB6B3hXQKmapM8GOldAUlGNJGLCV0Cy75/AIatYQGcR+khGMVlAQkcZ26ICWkDEZnxxtw1bQD7aRunG2VtAhNOKkpyiXEAFeBszLaldQA==",
            "dtype": "f8"
           }
          },
          {
           "mode": "lines",
           "name": "Cumulative donor contribution",
           "type": "scatter",
           "x": {
            "bdata": "1QfWB9cH2AfZB9oH2wfcB90H3gffB+AH4QfiB+MH5AflB+YH5wfoBw==",
            "dtype": "i2"
           },
           "y": {
            "bdata": "+yWIXup5T0AN6pozmQ1QQED9ZvMnZlBAL7g6aKqdUEDPsAJ7PcxQQNJvp/ow5lBAVvIlADLjUECKggKNGSNRQATv522kIFFAB98sbanzUEBTgvCy1hxRQNkBMIndH1FAqux6yPtiUUBs2FjPa2xRQCvjEKKfblFAXMiLt6a/UUBCumpxSNlRQGRcraNhPFJAfA4GkdZdUkBBKabXZZlSQA==",
            "dtype": "f8"
           }
          }
         ],
         "name": "4"
        },
        {
         "data": [
          {
           "mode": "lines",
           "name": "Colorado actual",
           "type": "scatter",
           "x": {
            "bdata": "1QfWB9cH2AfZB9oH2wfcB90H3gffB+AH4QfiB+MH5AflB+YH5wfoBw==",
            "dtype": "i2"
           },
           "y": {
            "bdata": "Tmrekm+GVUD6hfexxgBWQGIqrP9OhFZAUmcjTPLyVkAKxBs3zQRXQIZ6xqtkIFdA6R9NhZ/7VkCF8cHjdI1XQBX68wnLWVdAWXvKqjE4V0DPHyB6B3hXQKmapM8GOldAUlGNJGLCV0Cy75/AIatYQGcR+khGMVlAQkcZ26ICWkDEZnxxtw1bQD7aRunG2VtAhNOKkpyiXEAFeBszLaldQA==",
            "dtype": "f8"
           }
          },
          {
           "mode": "lines",
           "name": "Cumulative donor contribution",
           "type": "scatter",
           "x": {
            "bdata": "1QfWB9cH2AfZB9oH2wfcB90H3gffB+AH4QfiB+MH5AflB+YH5wfoBw==",
            "dtype": "i2"
           },
           "y": {
            "bdata": "9InvYyWNUUBOxmb3DOZRQAADXpOhR1JA2YwAUDmCUkBgV57k7rBSQP+v5sRg0FJAinP5x7jKUkBzJ67gwRBTQEFPOvLzCVNASnhOtn/UUkB5yLc5KgdTQFETugZhC1NAJ0oIC01LU0AA60+XnV5TQK7C4xI3aFNAVDf9p2a1U0DZzGLylt1TQDZ60iGDSFRA6ZgNUBRqVEBtjyhg8q5UQA==",
            "dtype": "f8"
           }
          }
         ],
         "name": "5"
        },
        {
         "data": [
          {
           "mode": "lines",
           "name": "Colorado actual",
           "type": "scatter",
           "x": {
            "bdata": "1QfWB9cH2AfZB9oH2wfcB90H3gffB+AH4QfiB+MH5AflB+YH5wfoBw==",
            "dtype": "i2"
           },
           "y": {
            "bdata": "Tmrekm+GVUD6hfexxgBWQGIqrP9OhFZAUmcjTPLyVkAKxBs3zQRXQIZ6xqtkIFdA6R9NhZ/7VkCF8cHjdI1XQBX68wnLWVdAWXvKqjE4V0DPHyB6B3hXQKmapM8GOldAUlGNJGLCV0Cy75/AIatYQGcR+khGMVlAQkcZ26ICWkDEZnxxtw1bQD7aRunG2VtAhNOKkpyiXEAFeBszLaldQA==",
            "dtype": "f8"
           }
          },
          {
           "mode": "lines",
           "name": "Cumulative donor contribution",
           "type": "scatter",
           "x": {
            "bdata": "1QfWB9cH2AfZB9oH2wfcB90H3gffB+AH4QfiB+MH5AflB+YH5wfoBw==",
            "dtype": "i2"
           },
           "y": {
            "bdata": "kncjgMuRU0A0P5lXNPdTQNsBgXINY1RAmOmr7w+kVED6lzyjKNRUQAr1Cey19VRAjcUDYpTnVED4f0ut/ztVQEF7gmNOO1VAgWCzQKD9VEDFBSsa3y9VQAZCCDoWN1VA6GBAyY55VUB5rYtahY5VQHPMmh6wm1VAClIa0LfyVUDPk+JlgBxWQLaLpc2DjlZAHgH+q9vFVkB+kyEYygxXQA==",
            "dtype": "f8"
           }
          }
         ],
         "name": "6"
        },
        {
         "data": [
          {
           "mode": "lines",
           "name": "Colorado actual",
           "type": "scatter",
           "x": {
            "bdata": "1QfWB9cH2AfZB9oH2wfcB90H3gffB+AH4QfiB+MH5AflB+YH5wfoBw==",
            "dtype": "i2"
           },
           "y": {
            "bdata": "Tmrekm+GVUD6hfexxgBWQGIqrP9OhFZAUmcjTPLyVkAKxBs3zQRXQIZ6xqtkIFdA6R9NhZ/7VkCF8cHjdI1XQBX68wnLWVdAWXvKqjE4V0DPHyB6B3hXQKmapM8GOldAUlGNJGLCV0Cy75/AIatYQGcR+khGMVlAQkcZ26ICWkDEZnxxtw1bQD7aRunG2VtAhNOKkpyiXEAFeBszLaldQA==",
            "dtype": "f8"
           }
          },
          {
           "mode": "lines",
           "name": "Cumulative donor contribution",
           "type": "scatter",
           "x": {
            "bdata": "1QfWB9cH2AfZB9oH2wfcB90H3gffB+AH4QfiB+MH5AflB+YH5wfoBw==",
            "dtype": "i2"
           },
           "y": {
            "bdata": "NGvpU4etVEDOnaKChhZVQLfKBKY9i1VAxVzyWt/SVUArcwWjNgRWQNG0eE0dLVZAy9hW8WIdVkC2coejZnFWQLfAES+GclZAPFXKCrczVkDv2K++v2VWQN5Ud3Csc1ZACFjgUse0VkB1kq78O89WQHGvPlRL4lZA3YRfLuRAV0DLauZfynFXQINVyFLj6VdAwQb4W3AiWED8CQq0EHZYQA==",
            "dtype": "f8"
           }
          }
         ],
         "name": "7"
        },
        {
         "data": [
          {
           "mode": "lines",
           "name": "Colorado actual",
           "type": "scatter",
           "x": {
            "bdata": "1QfWB9cH2AfZB9oH2wfcB90H3gffB+AH4QfiB+MH5AflB+YH5wfoBw==",
            "dtype": "i2"
           },
           "y": {
            "bdata": "Tmrekm+GVUD6hfexxgBWQGIqrP9OhFZAUmcjTPLyVkAKxBs3zQRXQIZ6xqtkIFdA6R9NhZ/7VkCF8cHjdI1XQBX68wnLWVdAWXvKqjE4V0DPHyB6B3hXQKmapM8GOldAUlGNJGLCV0Cy75/AIatYQGcR+khGMVlAQkcZ26ICWkDEZnxxtw1bQD7aRunG2VtAhNOKkpyiXEAFeBszLaldQA==",
            "dtype": "f8"
           }
          },
          {
           "mode": "lines",
           "name": "Cumulative donor contribution",
           "type": "scatter",
           "x": {
            "bdata": "1QfWB9cH2AfZB9oH2wfcB90H3gffB+AH4QfiB+MH5AflB+YH5wfoBw==",
            "dtype": "i2"
           },
           "y": {
            "bdata": "7uAha/buVED5p4yyflhVQLBc3LURz1VAMwBO8DIYVkB2vTUdJUpWQGKfqOpYdVZA9bJLMlhlVkDQRyIySLpWQG0G68dtu1ZABCWP1uF6VkCijUK1zK1WQJT2OpVpulZAM/a2H/38VkC4uZT52xhXQIbCBLM9K1dAQX8c86WKV0AHmusQ+bxXQIPGxDFPNlhAQ6B0qjBvWEBxvI186MNYQA==",
            "dtype": "f8"
           }
          }
         ],
         "name": "8"
        },
        {
         "data": [
          {
           "mode": "lines",
           "name": "Colorado actual",
           "type": "scatter",
           "x": {
            "bdata": "1QfWB9cH2AfZB9oH2wfcB90H3gffB+AH4QfiB+MH5AflB+YH5wfoBw==",
            "dtype": "i2"
           },
           "y": {
            "bdata": "Tmrekm+GVUD6hfexxgBWQGIqrP9OhFZAUmcjTPLyVkAKxBs3zQRXQIZ6xqtkIFdA6R9NhZ/7VkCF8cHjdI1XQBX68wnLWVdAWXvKqjE4V0DPHyB6B3hXQKmapM8GOldAUlGNJGLCV0Cy75/AIatYQGcR+khGMVlAQkcZ26ICWkDEZnxxtw1bQD7aRunG2VtAhNOKkpyiXEAFeBszLaldQA==",
            "dtype": "f8"
           }
          },
          {
           "mode": "lines",
           "name": "Cumulative donor contribution",
           "type": "scatter",
           "x": {
            "bdata": "1QfWB9cH2AfZB9oH2wfcB90H3gffB+AH4QfiB+MH5AflB+YH5wfoBw==",
            "dtype": "i2"
           },
           "y": {
            "bdata": "cu5pBrYzVUBDoRhdkpxVQI6DGKmvFVZAE8Ls+LBeVkD2S2N4dpFWQEONVsuVvVZA5+m0cq+sVkCuJ9qCIQJXQN7M0Y8QA1dAw053wdbCVkAbxIvkw/VWQKUmrHjIAldA5xDWqctEV0Df8DJ4ZmFXQCrSUUMvdFdAK1h1SLvUV0CABD9uMghYQL7x0EfFg1hANnc/jHu+WEDg4WSYoRRZQA==",
            "dtype": "f8"
           }
          }
         ],
         "name": "9"
        },
        {
         "data": [
          {
           "mode": "lines",
           "name": "Colorado actual",
           "type": "scatter",
           "x": {
            "bdata": "1QfWB9cH2AfZB9oH2wfcB90H3gffB+AH4QfiB+MH5AflB+YH5wfoBw==",
            "dtype": "i2"
           },
           "y": {
            "bdata": "Tmrekm+GVUD6hfexxgBWQGIqrP9OhFZAUmcjTPLyVkAKxBs3zQRXQIZ6xqtkIFdA6R9NhZ/7VkCF8cHjdI1XQBX68wnLWVdAWXvKqjE4V0DPHyB6B3hXQKmapM8GOldAUlGNJGLCV0Cy75/AIatYQGcR+khGMVlAQkcZ26ICWkDEZnxxtw1bQD7aRunG2VtAhNOKkpyiXEAFeBszLaldQA==",
            "dtype": "f8"
           }
          },
          {
           "mode": "lines",
           "name": "Cumulative donor contribution",
           "type": "scatter",
           "x": {
            "bdata": "1QfWB9cH2AfZB9oH2wfcB90H3gffB+AH4QfiB+MH5AflB+YH5wfoBw==",
            "dtype": "i2"
           },
           "y": {
            "bdata": "gIcKA81hVUDcg7DlGctVQJnmYoRBRVZA6w+L5UmPVkBkgBZn6MNWQJv7ekoP8VZAmSIDAKrgVkDUds/+XTVXQG45dLaiNldABlE9bjT2VkCHPn2ivypXQGn9sxePNldAPy+7V894V0CVyFYlQ5VXQEvcL6iLqFdAUsM3VL4IWEB0xL0U0DxYQArw+aS1uFhAPZzjPzXzWEDA1HyuQ0tZQA==",
            "dtype": "f8"
           }
          }
         ],
         "name": "10"
        },
        {
         "data": [
          {
           "mode": "lines",
           "name": "Colorado actual",
           "type": "scatter",
           "x": {
            "bdata": "1QfWB9cH2AfZB9oH2wfcB90H3gffB+AH4QfiB+MH5AflB+YH5wfoBw==",
            "dtype": "i2"
           },
           "y": {
            "bdata": "Tmrekm+GVUD6hfexxgBWQGIqrP9OhFZAUmcjTPLyVkAKxBs3zQRXQIZ6xqtkIFdA6R9NhZ/7VkCF8cHjdI1XQBX68wnLWVdAWXvKqjE4V0DPHyB6B3hXQKmapM8GOldAUlGNJGLCV0Cy75/AIatYQGcR+khGMVlAQkcZ26ICWkDEZnxxtw1bQD7aRunG2VtAhNOKkpyiXEAFeBszLaldQA==",
            "dtype": "f8"
           }
          },
          {
           "mode": "lines",
           "name": "Cumulative donor contribution",
           "type": "scatter",
           "x": {
            "bdata": "1QfWB9cH2AfZB9oH2wfcB90H3gffB+AH4QfiB+MH5AflB+YH5wfoBw==",
            "dtype": "i2"
           },
           "y": {
            "bdata": "Yvzf3BKXVUBLiSobaAFWQCaJx/mjfFZAcitWyw7IVkDeuyTUSf1WQLkxa3WnKldAd9SAYfkZV0DxADURfm5XQFQaRdvob1dAGJnnyXcvV0AkulE/kWRXQIrWNVoMcFdA1/mcOlGyV0A+jfOkuc9XQLcneNth5VdAG3YNkINFWEA+LyaT6XpYQEzQgDrp+FhAwKJ2ExM2WUArglEv0o5ZQA==",
            "dtype": "f8"
           }
          }
         ],
         "name": "11"
        }
       ],
       "layout": {
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0.0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1.0,
              "#f0f921"
             ]
            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0.0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1.0,
              "#f0f921"
             ]
            ],
            "type": "heatmap"
           }
          ],
          "histogram": [
           {
            "marker": {
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "histogram"
           }
          ],
          "histogram2d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0.0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1.0,
              "#f0f921"
             ]
            ],
            "type": "histogram2d"
           }
          ],
          "histogram2dcontour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0.0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1.0,
              "#f0f921"
             ]
            ],
            "type": "histogram2dcontour"
           }
          ],
          "mesh3d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "mesh3d"
           }
          ],
          "parcoords": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "parcoords"
           }
          ],
          "pie": [
           {
            "automargin": true,
            "type": "pie"
           }
          ],
          "scatter": [
           {
            "fillpattern": {
             "fillmode": "overlay",
             "size": 10,
             "solidity": 0.2
            },
            "type": "scatter"
           }
          ],
          "scatter3d": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter3d"
           }
          ],
          "scattercarpet": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattercarpet"
           }
          ],
          "scattergeo": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergeo"
           }
          ],
          "scattergl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergl"
           }
          ],
          "scattermap": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattermap"
           }
          ],
          "scattermapbox": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattermapbox"
           }
          ],
          "scatterpolar": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolar"
           }
          ],
          "scatterpolargl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolargl"
           }
          ],
          "scatterternary": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterternary"
           }
          ],
          "surface": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0.0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1.0,
              "#f0f921"
             ]
            ],
            "type": "surface"
           }
          ],
          "table": [
           {
            "cells": {
             "fill": {
              "color": "#EBF0F8"
             },
             "line": {
              "color": "white"
             }
            },
            "header": {
             "fill": {
              "color": "#C8D4E3"
             },
             "line": {
              "color": "white"
             }
            },
            "type": "table"
           }
          ]
         },
         "layout": {
          "annotationdefaults": {
           "arrowcolor": "#2a3f5f",
           "arrowhead": 0,
           "arrowwidth": 1
          },
          "autotypenumbers": "strict",
          "coloraxis": {
           "colorbar": {
            "outlinewidth": 0,
            "ticks": ""
           }
          },
          "colorscale": {
           "diverging": [
            [
             0,
             "#8e0152"
            ],
            [
             0.1,
             "#c51b7d"
            ],
            [
             0.2,
             "#de77ae"
            ],
            [
             0.3,
             "#f1b6da"
            ],
            [
             0.4,
             "#fde0ef"
            ],
            [
             0.5,
             "#f7f7f7"
            ],
            [
             0.6,
             "#e6f5d0"
            ],
            [
             0.7,
             "#b8e186"
            ],
            [
             0.8,
             "#7fbc41"
            ],
            [
             0.9,
             "#4d9221"
            ],
            [
             1,
             "#276419"
            ]
           ],
           "sequential": [
            [
             0.0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1.0,
             "#f0f921"
            ]
           ],
           "sequentialminus": [
            [
             0.0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1.0,
             "#f0f921"
            ]
           ]
          },
          "colorway": [
           "#636efa",
           "#EF553B",
           "#00cc96",
           "#ab63fa",
           "#FFA15A",
           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "How donor contributions build the synthetic path"
        },
        "updatemenus": [
         {
          "buttons": [
           {
            "args": [
             null,
             {
              "frame": {
               "duration": 700,
               "redraw": true
              },
              "fromcurrent": true
             }
            ],
            "label": "Play",
            "method": "animate"
           }
          ],
          "type": "buttons"
         }
        ],
        "xaxis": {
         "title": {
          "text": "Year"
         }
        },
        "yaxis": {
         "title": {
          "text": "Weighted outcome contribution"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if PLOTLY_AVAILABLE:\n",
    "    weighted_paths = []\n",
    "\n",
    "    for row in weight_table.itertuples(index=False):\n",
    "        if row.weight > 0.001:\n",
    "            donor_index = units.index(row.state)\n",
    "            weighted_paths.append(\n",
    "                (\n",
    "                    row.state,\n",
    "                    row.weight,\n",
    "                    Yall[:, donor_index] * row.weight,\n",
    "                )\n",
    "            )\n",
    "\n",
    "    cumulative = np.zeros_like(years, dtype=float)\n",
    "    frames = []\n",
    "\n",
    "    for i, (state, weight, contribution) in enumerate(weighted_paths, start=1):\n",
    "        cumulative = cumulative + contribution\n",
    "\n",
    "        frames.append(\n",
    "            go.Frame(\n",
    "                data=[\n",
    "                    go.Scatter(\n",
    "                        x=years,\n",
    "                        y=actual,\n",
    "                        mode=\"lines\",\n",
    "                        name=\"Colorado actual\",\n",
    "                    ),\n",
    "                    go.Scatter(\n",
    "                        x=years,\n",
    "                        y=cumulative.copy(),\n",
    "                        mode=\"lines\",\n",
    "                        name=\"Cumulative donor contribution\",\n",
    "                    ),\n",
    "                ],\n",
    "                name=str(i),\n",
    "            )\n",
    "        )\n",
    "\n",
    "    initial = np.zeros_like(years, dtype=float)\n",
    "\n",
    "    animated = go.Figure(\n",
    "        data=[\n",
    "            go.Scatter(\n",
    "                x=years,\n",
    "                y=actual,\n",
    "                mode=\"lines\",\n",
    "                name=\"Colorado actual\",\n",
    "            ),\n",
    "            go.Scatter(\n",
    "                x=years,\n",
    "                y=initial,\n",
    "                mode=\"lines\",\n",
    "                name=\"Cumulative donor contribution\",\n",
    "            ),\n",
    "        ],\n",
    "        frames=frames,\n",
    "    )\n",
    "\n",
    "    animated.update_layout(\n",
    "        title=\"How donor contributions build the synthetic path\",\n",
    "        xaxis_title=\"Year\",\n",
    "        yaxis_title=\"Weighted outcome contribution\",\n",
    "        updatemenus=[\n",
    "            {\n",
    "                \"type\": \"buttons\",\n",
    "                \"buttons\": [\n",
    "                    {\n",
    "                        \"label\": \"Play\",\n",
    "                        \"method\": \"animate\",\n",
    "                        \"args\": [\n",
    "                            None,\n",
    "                            {\n",
    "                                \"frame\": {\"duration\": 700, \"redraw\": True},\n",
    "                                \"fromcurrent\": True,\n",
    "                            },\n",
    "                        ],\n",
    "                    }\n",
    "                ],\n",
    "            }\n",
    "        ],\n",
    "    )\n",
    "\n",
    "    animated.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "775f8070",
   "metadata": {},
   "source": [
    "## 14. Export analysis tables and simulated data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1e0a20e9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-03T17:23:15.824877Z",
     "iopub.status.busy": "2026-09-03T17:23:15.824773Z",
     "iopub.status.idle": "2026-09-03T17:23:15.833969Z",
     "shell.execute_reply": "2026-09-03T17:23:15.833509Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved files to: /mnt/data/synthetic_control_outputs\n"
     ]
    }
   ],
   "source": [
    "panel.to_csv(OUTPUT_DIR / \"simulated_us_state_panel.csv\", index=False)\n",
    "weight_table.to_csv(OUTPUT_DIR / \"donor_weights.csv\", index=False)\n",
    "balance_table.to_csv(OUTPUT_DIR / \"predictor_balance.csv\", index=False)\n",
    "effect_path.to_csv(OUTPUT_DIR / \"effect_path.csv\", index=False)\n",
    "placebo_table.to_csv(OUTPUT_DIR / \"placebo_results.csv\", index=False)\n",
    "robustness_table.to_csv(OUTPUT_DIR / \"robustness_checks.csv\", index=False)\n",
    "\n",
    "print(\"Saved files to:\", OUTPUT_DIR.resolve())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cbbde7e",
   "metadata": {},
   "source": [
    "## 15. Final analysis summary\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "78b2101d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-03T17:23:15.835077Z",
     "iopub.status.busy": "2026-09-03T17:23:15.834975Z",
     "iopub.status.idle": "2026-09-03T17:23:15.841806Z",
     "shell.execute_reply": "2026-09-03T17:23:15.841304Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>metric</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Pre treatment RMSE</td>\n",
       "      <td>0.396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Average post treatment effect</td>\n",
       "      <td>9.641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Post to pre RMSPE ratio</td>\n",
       "      <td>26.719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Placebo rank</td>\n",
       "      <td>1 of 22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Rank based permutation value</td>\n",
       "      <td>0.045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Largest donor</td>\n",
       "      <td>Idaho</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Largest donor weight</td>\n",
       "      <td>0.394</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          metric    value\n",
       "0             Pre treatment RMSE    0.396\n",
       "1  Average post treatment effect    9.641\n",
       "2        Post to pre RMSPE ratio   26.719\n",
       "3                   Placebo rank  1 of 22\n",
       "4   Rank based permutation value    0.045\n",
       "5                  Largest donor    Idaho\n",
       "6           Largest donor weight    0.394"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_table = pd.DataFrame(\n",
    "    {\n",
    "        \"metric\": [\n",
    "            \"Pre treatment RMSE\",\n",
    "            \"Average post treatment effect\",\n",
    "            \"Post to pre RMSPE ratio\",\n",
    "            \"Placebo rank\",\n",
    "            \"Rank based permutation value\",\n",
    "            \"Largest donor\",\n",
    "            \"Largest donor weight\",\n",
    "        ],\n",
    "        \"value\": [\n",
    "            round(headline[\"pre_rmse\"], 3),\n",
    "            round(headline[\"avg_effect\"], 3),\n",
    "            round(headline[\"ratio\"], 3),\n",
    "            f\"{colorado_rank} of {len(placebo_table)}\",\n",
    "            round(placebo_p, 3),\n",
    "            weight_table.iloc[0][\"state\"],\n",
    "            round(weight_table.iloc[0][\"weight\"], 3),\n",
    "        ],\n",
    "    }\n",
    ")\n",
    "\n",
    "summary_table\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cf98cbd",
   "metadata": {},
   "source": [
    "## Interpretation\n",
    "\n",
    "In this simulated example, the synthetic path tracks Colorado closely before 2018. The observed outcome then rises above the synthetic counterfactual after the simulated intervention.\n",
    "\n",
    "The placebo analysis asks whether Colorado's post treatment divergence is unusually large relative to comparable placebo assignments. The robustness table checks whether reasonable modeling changes preserve the main direction of the result.\n",
    "\n",
    "For real policy evaluation, these diagnostics are necessary but not sufficient. The causal claim still depends on donor eligibility, intervention timing, spillovers, measurement consistency, and whether other shocks could explain the divergence.\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": "3.13.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}