diff --git a/data/tau_samples/spending_commerce_unemployment_rate_10_tau.pkl b/data/tau_samples/spending_commerce_unemployment_rate_10_tau.pkl
new file mode 100644
index 00000000..536d4768
Binary files /dev/null and b/data/tau_samples/spending_commerce_unemployment_rate_10_tau.pkl differ
diff --git a/docs/guides/counterfactual-explained.ipynb b/docs/guides/counterfactual-explained.ipynb
index e8e91067..f60fb50b 100644
--- a/docs/guides/counterfactual-explained.ipynb
+++ b/docs/guides/counterfactual-explained.ipynb
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -20,8 +20,7 @@
     "import plotly.express as px\n",
     "import seaborn as sns\n",
     "\n",
-    "from cities.queries.causal_insight_slim import \\\n",
-    "n-placeCausalInsightSlim as CausalInsight\n",
+    "from cities.queries.causal_insight_slim import CausalInsightSlim\n",
     "from cities.utils.data_grabber import (DataGrabber, find_repo_root,\n",
     "                                       list_available_features,\n",
     "                                       list_interventions, list_outcomes,\n",
@@ -34,12 +33,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Suppose you're interested in the impact of the Department of Commerce funding on the unemployment rate. Assume further that you're particularly interested in the fate of Northwest Arctic Borough, AK and what this county has potentially to gain from such funding. Say we want to estimate the causal role such a funding could have had if received in 2016. (this is because we can only fairly reliably estimate three years ahead, and we want to end the estimation period before the Covid pandemic, to avoid complications in the discussion of the result). Since you know it is unreasonable to expect such funding to have immediate impact, you allow for a one year of \"grace period\" and look at the outcome in 2018. The first thing you can you is load the data:"
+    "Suppose you're interested in the impact of the Department of Commerce funding on the unemployment rate. Assume further that you're particularly interested in the fate of Imperial County, CA and what this county has potentially to gain from such funding. Say we want to estimate the causal role such a funding could have had if received in 2016. (this is because we can only fairly reliably estimate three years ahead, and we want to end the estimation period before the Covid pandemic, to avoid complications in the discussion of the result). Since you know it is unreasonable to expect such funding to have immediate impact, you allow for a one year of \"grace period\" and look at the outcome in 2018. The first thing you can you is load the data:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -378,9 +377,344 @@
      },
      "metadata": {},
      "output_type": "display_data"
+    },
+    {
+     "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>GeoFIPS</th>\n",
+       "      <th>GeoName</th>\n",
+       "      <th>1990</th>\n",
+       "      <th>1991</th>\n",
+       "      <th>1992</th>\n",
+       "      <th>1993</th>\n",
+       "      <th>1994</th>\n",
+       "      <th>1995</th>\n",
+       "      <th>1996</th>\n",
+       "      <th>1997</th>\n",
+       "      <th>...</th>\n",
+       "      <th>2013</th>\n",
+       "      <th>2014</th>\n",
+       "      <th>2015</th>\n",
+       "      <th>2016</th>\n",
+       "      <th>2017</th>\n",
+       "      <th>2018</th>\n",
+       "      <th>2019</th>\n",
+       "      <th>2020</th>\n",
+       "      <th>2021</th>\n",
+       "      <th>2022</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>190</th>\n",
+       "      <td>6025</td>\n",
+       "      <td>Imperial County, CA</td>\n",
+       "      <td>25.4</td>\n",
+       "      <td>26.3</td>\n",
+       "      <td>30.9</td>\n",
+       "      <td>29.4</td>\n",
+       "      <td>26.5</td>\n",
+       "      <td>29.2</td>\n",
+       "      <td>30.1</td>\n",
+       "      <td>26.9</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.3</td>\n",
+       "      <td>24.3</td>\n",
+       "      <td>24.6</td>\n",
+       "      <td>24.2</td>\n",
+       "      <td>19.7</td>\n",
+       "      <td>18.8</td>\n",
+       "      <td>20.7</td>\n",
+       "      <td>22.6</td>\n",
+       "      <td>17.5</td>\n",
+       "      <td>14.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1058</th>\n",
+       "      <td>21153</td>\n",
+       "      <td>Magoffin County, KY</td>\n",
+       "      <td>15.5</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>16.1</td>\n",
+       "      <td>15.4</td>\n",
+       "      <td>13.7</td>\n",
+       "      <td>15.2</td>\n",
+       "      <td>14.6</td>\n",
+       "      <td>13.9</td>\n",
+       "      <td>...</td>\n",
+       "      <td>17.4</td>\n",
+       "      <td>13.9</td>\n",
+       "      <td>15.6</td>\n",
+       "      <td>19.6</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>12.6</td>\n",
+       "      <td>10.5</td>\n",
+       "      <td>15.6</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>10.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>102</th>\n",
+       "      <td>4027</td>\n",
+       "      <td>Yuma County, AZ</td>\n",
+       "      <td>20.6</td>\n",
+       "      <td>20.9</td>\n",
+       "      <td>26.6</td>\n",
+       "      <td>27.9</td>\n",
+       "      <td>31.3</td>\n",
+       "      <td>29.5</td>\n",
+       "      <td>30.6</td>\n",
+       "      <td>27.5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.6</td>\n",
+       "      <td>22.9</td>\n",
+       "      <td>21.8</td>\n",
+       "      <td>19.1</td>\n",
+       "      <td>17.2</td>\n",
+       "      <td>16.9</td>\n",
+       "      <td>16.7</td>\n",
+       "      <td>17.1</td>\n",
+       "      <td>13.1</td>\n",
+       "      <td>12.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>87</th>\n",
+       "      <td>2290</td>\n",
+       "      <td>Yukon-Koyukuk Census Area, AK</td>\n",
+       "      <td>14.6</td>\n",
+       "      <td>16.1</td>\n",
+       "      <td>17.7</td>\n",
+       "      <td>15.2</td>\n",
+       "      <td>16.2</td>\n",
+       "      <td>17.5</td>\n",
+       "      <td>17.1</td>\n",
+       "      <td>14.2</td>\n",
+       "      <td>...</td>\n",
+       "      <td>18.4</td>\n",
+       "      <td>18.7</td>\n",
+       "      <td>17.8</td>\n",
+       "      <td>17.2</td>\n",
+       "      <td>16.6</td>\n",
+       "      <td>13.7</td>\n",
+       "      <td>13.1</td>\n",
+       "      <td>12.7</td>\n",
+       "      <td>12.3</td>\n",
+       "      <td>7.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>183</th>\n",
+       "      <td>6011</td>\n",
+       "      <td>Colusa County, CA</td>\n",
+       "      <td>14.2</td>\n",
+       "      <td>18.1</td>\n",
+       "      <td>21.7</td>\n",
+       "      <td>21.7</td>\n",
+       "      <td>19.2</td>\n",
+       "      <td>19.7</td>\n",
+       "      <td>20.1</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>19.1</td>\n",
+       "      <td>17.6</td>\n",
+       "      <td>15.5</td>\n",
+       "      <td>15.6</td>\n",
+       "      <td>14.3</td>\n",
+       "      <td>13.2</td>\n",
+       "      <td>12.9</td>\n",
+       "      <td>16.4</td>\n",
+       "      <td>12.5</td>\n",
+       "      <td>11.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>84</th>\n",
+       "      <td>2188</td>\n",
+       "      <td>Northwest Arctic Borough, AK</td>\n",
+       "      <td>14.9</td>\n",
+       "      <td>15.7</td>\n",
+       "      <td>20.2</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>15.5</td>\n",
+       "      <td>16.3</td>\n",
+       "      <td>17.6</td>\n",
+       "      <td>14.3</td>\n",
+       "      <td>...</td>\n",
+       "      <td>16.5</td>\n",
+       "      <td>16.8</td>\n",
+       "      <td>15.5</td>\n",
+       "      <td>15.4</td>\n",
+       "      <td>15.2</td>\n",
+       "      <td>12.8</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>12.7</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1414</th>\n",
+       "      <td>28063</td>\n",
+       "      <td>Jefferson County, MS</td>\n",
+       "      <td>22.9</td>\n",
+       "      <td>28.4</td>\n",
+       "      <td>20.9</td>\n",
+       "      <td>17.8</td>\n",
+       "      <td>17.8</td>\n",
+       "      <td>15.5</td>\n",
+       "      <td>15.3</td>\n",
+       "      <td>15.7</td>\n",
+       "      <td>...</td>\n",
+       "      <td>19.2</td>\n",
+       "      <td>16.3</td>\n",
+       "      <td>15.1</td>\n",
+       "      <td>15.2</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>13.4</td>\n",
+       "      <td>15.9</td>\n",
+       "      <td>18.2</td>\n",
+       "      <td>15.8</td>\n",
+       "      <td>13.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1793</th>\n",
+       "      <td>35029</td>\n",
+       "      <td>Luna County, NM</td>\n",
+       "      <td>13.1</td>\n",
+       "      <td>15.1</td>\n",
+       "      <td>16.7</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>24.8</td>\n",
+       "      <td>26.9</td>\n",
+       "      <td>25.7</td>\n",
+       "      <td>...</td>\n",
+       "      <td>18.9</td>\n",
+       "      <td>17.9</td>\n",
+       "      <td>17.5</td>\n",
+       "      <td>14.6</td>\n",
+       "      <td>13.9</td>\n",
+       "      <td>11.8</td>\n",
+       "      <td>12.3</td>\n",
+       "      <td>15.4</td>\n",
+       "      <td>15.3</td>\n",
+       "      <td>9.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>65</th>\n",
+       "      <td>1131</td>\n",
+       "      <td>Wilcox County, AL</td>\n",
+       "      <td>12.5</td>\n",
+       "      <td>15.6</td>\n",
+       "      <td>17.9</td>\n",
+       "      <td>17.5</td>\n",
+       "      <td>15.3</td>\n",
+       "      <td>15.6</td>\n",
+       "      <td>14.1</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>19.5</td>\n",
+       "      <td>16.8</td>\n",
+       "      <td>14.7</td>\n",
+       "      <td>14.5</td>\n",
+       "      <td>11.7</td>\n",
+       "      <td>9.5</td>\n",
+       "      <td>7.5</td>\n",
+       "      <td>15.9</td>\n",
+       "      <td>10.5</td>\n",
+       "      <td>7.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1410</th>\n",
+       "      <td>28055</td>\n",
+       "      <td>Issaquena County, MS</td>\n",
+       "      <td>11.9</td>\n",
+       "      <td>13.4</td>\n",
+       "      <td>12.9</td>\n",
+       "      <td>13.5</td>\n",
+       "      <td>14.8</td>\n",
+       "      <td>16.1</td>\n",
+       "      <td>17.9</td>\n",
+       "      <td>14.5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>20.4</td>\n",
+       "      <td>17.6</td>\n",
+       "      <td>17.2</td>\n",
+       "      <td>14.3</td>\n",
+       "      <td>12.2</td>\n",
+       "      <td>9.2</td>\n",
+       "      <td>12.2</td>\n",
+       "      <td>8.9</td>\n",
+       "      <td>8.9</td>\n",
+       "      <td>7.3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10 rows × 35 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      GeoFIPS                        GeoName  1990  1991  1992  1993  1994  \\\n",
+       "190      6025            Imperial County, CA  25.4  26.3  30.9  29.4  26.5   \n",
+       "1058    21153            Magoffin County, KY  15.5  18.0  16.1  15.4  13.7   \n",
+       "102      4027                Yuma County, AZ  20.6  20.9  26.6  27.9  31.3   \n",
+       "87       2290  Yukon-Koyukuk Census Area, AK  14.6  16.1  17.7  15.2  16.2   \n",
+       "183      6011              Colusa County, CA  14.2  18.1  21.7  21.7  19.2   \n",
+       "84       2188   Northwest Arctic Borough, AK  14.9  15.7  20.2  17.0  15.5   \n",
+       "1414    28063           Jefferson County, MS  22.9  28.4  20.9  17.8  17.8   \n",
+       "1793    35029                Luna County, NM  13.1  15.1  16.7  16.0  21.0   \n",
+       "65       1131              Wilcox County, AL  12.5  15.6  17.9  17.5  15.3   \n",
+       "1410    28055           Issaquena County, MS  11.9  13.4  12.9  13.5  14.8   \n",
+       "\n",
+       "      1995  1996  1997  ...  2013  2014  2015  2016  2017  2018  2019  2020  \\\n",
+       "190   29.2  30.1  26.9  ...  25.3  24.3  24.6  24.2  19.7  18.8  20.7  22.6   \n",
+       "1058  15.2  14.6  13.9  ...  17.4  13.9  15.6  19.6  16.0  12.6  10.5  15.6   \n",
+       "102   29.5  30.6  27.5  ...  25.6  22.9  21.8  19.1  17.2  16.9  16.7  17.1   \n",
+       "87    17.5  17.1  14.2  ...  18.4  18.7  17.8  17.2  16.6  13.7  13.1  12.7   \n",
+       "183   19.7  20.1  19.0  ...  19.1  17.6  15.5  15.6  14.3  13.2  12.9  16.4   \n",
+       "84    16.3  17.6  14.3  ...  16.5  16.8  15.5  15.4  15.2  12.8  13.0  12.7   \n",
+       "1414  15.5  15.3  15.7  ...  19.2  16.3  15.1  15.2  15.0  13.4  15.9  18.2   \n",
+       "1793  24.8  26.9  25.7  ...  18.9  17.9  17.5  14.6  13.9  11.8  12.3  15.4   \n",
+       "65    15.6  14.1  14.0  ...  19.5  16.8  14.7  14.5  11.7   9.5   7.5  15.9   \n",
+       "1410  16.1  17.9  14.5  ...  20.4  17.6  17.2  14.3  12.2   9.2  12.2   8.9   \n",
+       "\n",
+       "      2021  2022  \n",
+       "190   17.5  14.7  \n",
+       "1058  11.5  10.5  \n",
+       "102   13.1  12.5  \n",
+       "87    12.3   7.9  \n",
+       "183   12.5  11.6  \n",
+       "84    11.5   9.0  \n",
+       "1414  15.8  13.0  \n",
+       "1793  15.3   9.9  \n",
+       "65    10.5   7.8  \n",
+       "1410   8.9   7.3  \n",
+       "\n",
+       "[10 rows x 35 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
+    "CausalInsight = CausalInsightSlim  # hacky way of avoiding isort complaining\n",
+    "\n",
     "outcome = \"unemployment_rate\"\n",
     "intervention = \"spending_commerce\"\n",
     "forward_shift = 2\n",
@@ -388,7 +722,10 @@
     "dg = DataGrabber()\n",
     "dg.get_features_wide([outcome, intervention])\n",
     "\n",
-    "display(dg.wide[intervention].head(), dg.wide[outcome].head())"
+    "display(dg.wide[intervention].head(), dg.wide[outcome].head())\n",
+    "\n",
+    "sorted = dg.wide[outcome].sort_values(by=\"2016\", ascending=False)\n",
+    "display(sorted.head(10))"
    ]
   },
   {
@@ -400,7 +737,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -413,7 +750,7 @@
         {
          "alignmentgroup": "True",
          "boxpoints": "all",
-         "hovertemplate": "North Arctic Borough, AK=False<br>Group=%{x}<br>out_data=%{y}<extra></extra>",
+         "hovertemplate": "Imperial County, CA=False<br>Group=%{x}<br>out_data=%{y}<extra></extra>",
          "legendgroup": "False",
          "marker": {
           "color": "#636efa"
@@ -2922,6 +3259,7 @@
           5.5,
           10.2,
           6.9,
+          12.8,
           8.9,
           13.7,
           5.8,
@@ -3000,7 +3338,6 @@
           4,
           13.2,
           6.6,
-          18.8,
           3.9,
           8.1,
           5.3,
@@ -5296,7 +5633,7 @@
         {
          "alignmentgroup": "True",
          "boxpoints": "all",
-         "hovertemplate": "North Arctic Borough, AK=True<br>Group=%{x}<br>out_data=%{y}<extra></extra>",
+         "hovertemplate": "Imperial County, CA=True<br>Group=%{x}<br>out_data=%{y}<extra></extra>",
          "legendgroup": "True",
          "marker": {
           "color": "#EF553B"
@@ -5313,7 +5650,7 @@
          "x0": " ",
          "xaxis": "x",
          "y": [
-          12.8
+          18.8
          ],
          "y0": " ",
          "yaxis": "y"
@@ -5333,9 +5670,9 @@
            "size": 10
           },
           "showarrow": false,
-          "text": "North Arctic Borough, AK",
-          "x": 0.26,
-          "y": 13.5
+          "text": "Imperial County, CA",
+          "x": 0.18,
+          "y": 18
          },
          {
           "showarrow": false,
@@ -5347,7 +5684,7 @@
         "boxmode": "group",
         "legend": {
          "title": {
-          "text": "North Arctic Borough, AK"
+          "text": "Imperial County, CA"
          },
          "tracegroupgap": 0
         },
@@ -5496,15 +5833,14 @@
     }
    ],
    "source": [
-    "# Northwest Arctic Borough, AK, fips = 2188\n",
-    "\n",
     "int_data = dg.wide[intervention][\"2016\"]\n",
     "out_data = dg.wide[outcome][\"2018\"]\n",
     "\n",
     "unfunded_mask = int_data <= 0\n",
     "moderately_funded_mask = int_data > 1 * 1e8\n",
     "unfunded_data = out_data[unfunded_mask]\n",
-    "unfunded_fips_2188_mask = dg.wide[outcome][\"GeoFIPS\"][unfunded_mask] == 2188\n",
+    "\n",
+    "unfunded_fips_6025_mask = dg.wide[outcome][\"GeoFIPS\"][unfunded_mask] == 6025\n",
     "moderately_data = out_data[moderately_funded_mask]\n",
     "false_array = np.tile(False, len(moderately_data))\n",
     "\n",
@@ -5513,9 +5849,7 @@
     "        \"Group\": [\"no funding\"] * len(unfunded_data)\n",
     "        + [\"$1B < funding\"] * len(moderately_data),\n",
     "        \"out_data\": np.concatenate([unfunded_data, moderately_data]),\n",
-    "        \"North Arctic Borough, AK\": np.concatenate(\n",
-    "            [unfunded_fips_2188_mask, false_array]\n",
-    "        ),\n",
+    "        \"Imperial County, CA\": np.concatenate([unfunded_fips_6025_mask, false_array]),\n",
     "    }\n",
     ")\n",
     "\n",
@@ -5525,7 +5859,7 @@
     "    x=\"Group\",\n",
     "    y=\"out_data\",\n",
     "    points=\"all\",\n",
-    "    color=\"North Arctic Borough, AK\",\n",
+    "    color=\"Imperial County, CA\",\n",
     "    title=\"The unfunded vs. the funded (2016 vs. 2018)\",\n",
     ")\n",
     "mean_unfunded = unfunded_data.mean()\n",
@@ -5534,9 +5868,9 @@
     "    x=0.3, y=mean_unfunded, text=f\"Mean: {mean_unfunded:.2f}\", showarrow=False\n",
     ")\n",
     "fig.add_annotation(\n",
-    "    x=0.26,\n",
-    "    y=13.5,\n",
-    "    text=\"North Arctic Borough, AK\",\n",
+    "    x=0.18,\n",
+    "    y=18,\n",
+    "    text=\"Imperial County, CA\",\n",
     "    font=dict(size=10, color=\"red\"),\n",
     "    showarrow=False,\n",
     ")\n",
@@ -5557,7 +5891,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -5612,7 +5946,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "There are, however, reasons to think that this way of thinking is a bit too hasty. The key problem is that neither U.S. Department of Commerce funding, nor unemployment arise in vacuum. For one thing, obviously, various covariates should be paid attention to, and their use might lead to more precise predictions that would take the specific circumstances of this county into consideration. The problem is deeper though: the protection effect that you think you see might simply arise because the places with high unemployment are under-staffed, under-funded or for some reasons ineligible for the department of commerce funding. The problem then is that the funded places more or less self-selected because some of their covariates caused the unfunded places to remain unfunded. You need to pay attention not only to covariates but also to the causal structure of the problem. This is what our model does, insofar as general application to all intput-output combinations permits."
+    "There are, however, reasons to think that this is a bit too hasty. The key problem is that neither U.S. Department of Commerce funding, nor unemployment arise in vacuum. For one thing, obviously, various covariates should be paid attention to, and their use might lead to more precise predictions that would take the specific circumstances of this county into consideration. The problem is deeper though: the protection effect that you think you see might simply arise because the places with high unemployment are under-staffed, under-funded or for some reasons ineligible for the department of commerce funding. The problem then is that the funded places more or less self-selected because some of their covariates caused the unfunded places to remain unfunded. You need to pay attention not only to covariates but also to the causal structure of the problem. This is what our model does, insofar as general application to all intput-output combinations permits."
    ]
   },
   {
@@ -5624,7 +5958,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -5663,12 +5997,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x800 with 1 Axes>"
       ]
@@ -5723,7 +6057,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -5758,34 +6092,34 @@
        "    <tr>\n",
        "      <th>2016</th>\n",
        "      <td>2016</td>\n",
-       "      <td>15.4</td>\n",
-       "      <td>15.4</td>\n",
-       "      <td>15.4</td>\n",
-       "      <td>15.4</td>\n",
+       "      <td>24.2</td>\n",
+       "      <td>24.2</td>\n",
+       "      <td>24.2</td>\n",
+       "      <td>24.2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2017</th>\n",
        "      <td>2017</td>\n",
-       "      <td>15.2</td>\n",
-       "      <td>14.023429</td>\n",
-       "      <td>13.612568</td>\n",
-       "      <td>14.465484</td>\n",
+       "      <td>19.7</td>\n",
+       "      <td>18.520342</td>\n",
+       "      <td>17.716211</td>\n",
+       "      <td>19.459003</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2018</th>\n",
        "      <td>2018</td>\n",
-       "      <td>12.8</td>\n",
-       "      <td>11.957299</td>\n",
-       "      <td>11.601073</td>\n",
-       "      <td>12.349377</td>\n",
+       "      <td>18.8</td>\n",
+       "      <td>17.467766</td>\n",
+       "      <td>16.659423</td>\n",
+       "      <td>18.46296</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2019</th>\n",
        "      <td>2019</td>\n",
-       "      <td>13.0</td>\n",
-       "      <td>12.116861</td>\n",
-       "      <td>11.757838</td>\n",
-       "      <td>12.492489</td>\n",
+       "      <td>20.7</td>\n",
+       "      <td>18.389801</td>\n",
+       "      <td>17.338502</td>\n",
+       "      <td>19.864349</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -5793,10 +6127,10 @@
       ],
       "text/plain": [
        "      year observed       mean        low       high\n",
-       "2016  2016     15.4       15.4       15.4       15.4\n",
-       "2017  2017     15.2  14.023429  13.612568  14.465484\n",
-       "2018  2018     12.8  11.957299  11.601073  12.349377\n",
-       "2019  2019     13.0  12.116861  11.757838  12.492489"
+       "2016  2016     24.2       24.2       24.2       24.2\n",
+       "2017  2017     19.7  18.520342  17.716211  19.459003\n",
+       "2018  2018     18.8  17.467766  16.659423   18.46296\n",
+       "2019  2019     20.7  18.389801  17.338502  19.864349"
       ]
      },
      "metadata": {},
@@ -5815,9 +6149,9 @@
           "width": 3
          },
          "mode": "lines+markers",
-         "name": "Northwest Arctic Borough, AK",
+         "name": "Imperial County, CA",
          "showlegend": false,
-         "text": "Northwest Arctic Borough, AK",
+         "text": "Imperial County, CA",
          "textposition": "top right",
          "type": "scatter",
          "x": [
@@ -5856,39 +6190,39 @@
           2022
          ],
          "y": [
-          14.9,
+          25.4,
+          26.3,
+          30.9,
+          29.4,
+          26.5,
+          29.2,
+          30.1,
+          26.9,
+          26,
+          23.7,
+          17.3,
+          16,
+          15.1,
           15.7,
-          20.2,
           17,
-          15.5,
-          16.3,
-          17.6,
-          14.3,
-          11.7,
-          13.7,
-          9,
-          9.8,
-          11.3,
-          13,
-          13.3,
-          11.9,
-          11.4,
-          10.7,
-          11.2,
-          12.2,
-          13.9,
-          15.8,
           15.9,
-          16.5,
-          16.8,
-          15.5,
           15.4,
-          15.2,
-          12.8,
-          13,
-          12.7,
-          11.5,
-          9
+          17.9,
+          22.6,
+          28.3,
+          29.4,
+          29.3,
+          27.7,
+          25.3,
+          24.3,
+          24.6,
+          24.2,
+          19.7,
+          18.8,
+          20.7,
+          22.6,
+          17.5,
+          14.7
          ]
         },
         {
@@ -5899,10 +6233,10 @@
          "mode": "lines",
          "name": "mean prediction",
          "text": [
-          15.4,
-          14.02342876567345,
-          11.957298947940416,
-          12.116861408103025
+          24.2,
+          18.520342129678873,
+          17.4677660898787,
+          18.38980072370943
          ],
          "type": "scatter",
          "x": [
@@ -5912,10 +6246,10 @@
           "2019"
          ],
          "y": [
-          15.4,
-          14.02342876567345,
-          11.957298947940416,
-          12.116861408103025
+          24.2,
+          18.520342129678873,
+          17.4677660898787,
+          18.38980072370943
          ]
         },
         {
@@ -5937,14 +6271,14 @@
           "2016"
          ],
          "y": [
-          15.4,
-          14.465484102297477,
-          12.349376535175548,
-          12.492488781565942,
-          11.757838022182563,
-          11.601073081843463,
-          13.612567619256561,
-          15.4
+          24.2,
+          19.45900278443401,
+          18.462959722422184,
+          19.864348591412533,
+          17.33850235453402,
+          16.659423290772093,
+          17.716210592385732,
+          24.2
          ]
         }
        ],
@@ -6818,7 +7152,7 @@
          "font": {
           "size": 12
          },
-         "text": "Predicted unemployment_rate in Northwest Arctic Borough, AK<br>under intervention [3.0047122e+09] in year 2016<br>99.739% of counties received a lower intervention <br>observed intervention: 0.0"
+         "text": "Predicted unemployment_rate in Imperial, CA<br>under intervention [3.0047122e+09] in year 2016<br>99.739% of counties received a lower intervention <br>observed intervention: 0.0"
         },
         "xaxis": {
          "title": {
@@ -6827,8 +7161,8 @@
         },
         "yaxis": {
          "range": [
-          7.2,
-          26.26
+          11.76,
+          40.17
          ],
          "title": {
           "text": "Value"
@@ -6851,7 +7185,7 @@
     "percent_calc = ci.slider_values_to_interventions(intervened_percent=45, year=2016)\n",
     "ci.get_tau_samples()\n",
     "\n",
-    "fips = 2188\n",
+    "fips = 6025  # 2188\n",
     "ci.get_fips_predictions(\n",
     "    intervened_value=percent_calc[\"intervened_transformed\"], fips=fips, year=2016\n",
     ")\n",
@@ -6868,7 +7202,7 @@
     "Recall now our original question inspired by the initial and misleading data visualisation. Recall that our question was, *which is it, should you expect a drop of \n",
     ".6% if you get around $3B funding, or should you expect to be protected from the extreme outcomes and hope for an outcome closer to 6%?*\n",
     "\n",
-    "The more causally aware our models offer is that neither answer is correct. On one hand, there are some confunding factors that lead to self-selection of funded counties, so the latter belief would be overly optimistic. On the other hand, you should also expect a slightly higher difference, that of .9% rather than .6%."
+    "The more causally aware our models offer is that neither answer is correct. On one hand, there are some confunding factors that lead to self-selection of funded counties, so the latter belief would be overly optimistic. On the other hand, you should also expect a slightly higher difference, that of 1.2% rather than .6%."
    ]
   }
  ],
diff --git a/docs/experimental_notebooks/causal_insights_demo.ipynb b/docs/testing_notebooks/causal_insights_demo.ipynb
similarity index 98%
rename from docs/experimental_notebooks/causal_insights_demo.ipynb
rename to docs/testing_notebooks/causal_insights_demo.ipynb
index 7746cbea..61c1d424 100644
--- a/docs/experimental_notebooks/causal_insights_demo.ipynb
+++ b/docs/testing_notebooks/causal_insights_demo.ipynb
@@ -4,20 +4,7 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "ModuleNotFoundError",
-     "evalue": "No module named 'pyro'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[1], line 4\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mos\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mrandom\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcities\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mqueries\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcausal_insight\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m CausalInsight\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcities\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdata_grabber\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (DataGrabber, list_interventions,\n\u001b[1;32m      6\u001b[0m                                        list_outcomes)\n\u001b[1;32m      8\u001b[0m smoke_test \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCI\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m os\u001b[38;5;241m.\u001b[39menviron\n",
-      "File \u001b[0;32m~/s78projects/cities/cities/queries/causal_insight.py:8\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mplotly\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgraph_objects\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mgo\u001b[39;00m\n\u001b[0;32m----> 8\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpyro\u001b[39;00m\n\u001b[1;32m      9\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\n\u001b[1;32m     10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpreprocessing\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m StandardScaler\n",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'pyro'"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import os\n",
     "import random\n",
diff --git a/docs/experimental_notebooks/slider.ipynb b/docs/testing_notebooks/slider.ipynb
similarity index 100%
rename from docs/experimental_notebooks/slider.ipynb
rename to docs/testing_notebooks/slider.ipynb
diff --git a/docs/experimental_notebooks/slimming/slim_insight.ipynb b/docs/testing_notebooks/slim_insight.ipynb
similarity index 100%
rename from docs/experimental_notebooks/slimming/slim_insight.ipynb
rename to docs/testing_notebooks/slim_insight.ipynb