diff --git a/docs/source/example_notebooks/counterfactual_fairness_dowhy.ipynb b/docs/source/example_notebooks/counterfactual_fairness_dowhy.ipynb
index 6cf50e14f..44d07c424 100644
--- a/docs/source/example_notebooks/counterfactual_fairness_dowhy.ipynb
+++ b/docs/source/example_notebooks/counterfactual_fairness_dowhy.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "ef2e40dc",
    "metadata": {},
    "outputs": [],
@@ -52,7 +52,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "46026b9b",
    "metadata": {},
    "outputs": [],
@@ -247,7 +247,6 @@
     "    plt.show()\n",
     "\n",
     "\n",
-    "\n",
     "def _wrapper_lambda_fn(val):\n",
     "    return lambda x: val"
    ]
@@ -279,97 +278,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "37212ae1",
    "metadata": {},
-   "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>Race</th>\n",
-       "      <th>Gender</th>\n",
-       "      <th>GPA</th>\n",
-       "      <th>LSAT</th>\n",
-       "      <th>avg_grade</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.9</td>\n",
-       "      <td>43.0</td>\n",
-       "      <td>0.59</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.7</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>0.25</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>3.9</td>\n",
-       "      <td>34.0</td>\n",
-       "      <td>0.57</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.7</td>\n",
-       "      <td>42.0</td>\n",
-       "      <td>-0.67</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.9</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>0.04</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Race  Gender  GPA  LSAT  avg_grade\n",
-       "0   0.0     0.0  2.9  43.0       0.59\n",
-       "1   0.0     0.0  2.7  31.0       0.25\n",
-       "2   0.0     1.0  3.9  34.0       0.57\n",
-       "3   0.0     0.0  2.7  42.0      -0.67\n",
-       "4   1.0     1.0  2.9  14.0       0.04"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df = pd.read_csv(\"datasets/law_data.csv\")\n",
     "\n",
@@ -442,7 +354,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "2280c2df",
    "metadata": {},
    "outputs": [],
@@ -475,7 +387,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "d70d0187",
    "metadata": {},
    "outputs": [],
@@ -498,32 +410,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "6fdaa7be",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Fitting causal mechanism of node Gender: 100%|██████████| 5/5 [00:00<00:00, 347.95it/s]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/latex": [
-       "$\\displaystyle -0.520187019335947$"
-      ],
-      "text/plain": [
-       "-0.5201870193359467"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "config = {\n",
     "    \"df\": df_sample,\n",
@@ -550,103 +440,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "79c2a233",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Race</th>\n",
-       "      <th>Gender</th>\n",
-       "      <th>GPA</th>\n",
-       "      <th>LSAT</th>\n",
-       "      <th>avg_grade</th>\n",
-       "      <th>preds</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.9</td>\n",
-       "      <td>43.0</td>\n",
-       "      <td>0.59</td>\n",
-       "      <td>0.315093</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.7</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>0.25</td>\n",
-       "      <td>-0.262915</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>3.9</td>\n",
-       "      <td>34.0</td>\n",
-       "      <td>0.57</td>\n",
-       "      <td>0.227097</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.7</td>\n",
-       "      <td>42.0</td>\n",
-       "      <td>-0.67</td>\n",
-       "      <td>0.211845</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.9</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>0.04</td>\n",
-       "      <td>-0.936546</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Race  Gender  GPA  LSAT  avg_grade     preds\n",
-       "0   0.0     0.0  2.9  43.0       0.59  0.315093\n",
-       "1   0.0     0.0  2.7  31.0       0.25 -0.262915\n",
-       "2   0.0     1.0  3.9  34.0       0.57  0.227097\n",
-       "3   0.0     0.0  2.7  42.0      -0.67  0.211845\n",
-       "4   1.0     1.0  2.9  14.0       0.04 -0.936546"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df_obs.head()"
    ]
@@ -661,124 +458,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "56d88825",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "    .dataframe tbody tr th {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Race</th>\n",
-       "      <th>Gender</th>\n",
-       "      <th>GPA</th>\n",
-       "      <th>LSAT</th>\n",
-       "      <th>avg_grade</th>\n",
-       "      <th>preds_cf</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.544718</td>\n",
-       "      <td>35.134519</td>\n",
-       "      <td>-0.275034</td>\n",
-       "      <td>0.153322</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.344718</td>\n",
-       "      <td>23.134519</td>\n",
-       "      <td>-0.538674</td>\n",
-       "      <td>0.021344</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>3.544718</td>\n",
-       "      <td>26.134519</td>\n",
-       "      <td>-0.749413</td>\n",
-       "      <td>0.192129</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.344718</td>\n",
-       "      <td>34.134519</td>\n",
-       "      <td>-1.466707</td>\n",
-       "      <td>0.120357</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>3.255282</td>\n",
-       "      <td>21.865481</td>\n",
-       "      <td>1.132647</td>\n",
-       "      <td>0.119023</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Race  Gender       GPA       LSAT  avg_grade  preds_cf\n",
-       "0   1.0     0.0  2.544718  35.134519  -0.275034  0.153322\n",
-       "1   1.0     0.0  2.344718  23.134519  -0.538674  0.021344\n",
-       "2   1.0     1.0  3.544718  26.134519  -0.749413  0.192129\n",
-       "3   1.0     0.0  2.344718  34.134519  -1.466707  0.120357\n",
-       "4   0.0     1.0  3.255282  21.865481   1.132647  0.119023"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df_cf.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "a1176177",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plot_counterfactual_fairness(\n",
     "    df_obs=df_obs,\n",
@@ -806,32 +499,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "725bf447",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Fitting causal mechanism of node Gender: 100%|██████████| 5/5 [00:00<00:00, 467.03it/s]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/latex": [
-       "$\\displaystyle -0.509033121401071$"
-      ],
-      "text/plain": [
-       "-0.5090331214010707"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "disadvantage_group = {\"Race\": 1, \"Gender\": 1}\n",
     "config = {\n",
@@ -852,21 +523,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "ad7671e5",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plot_counterfactual_fairness(\n",
     "    df_obs=df_obs,\n",
@@ -922,32 +582,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "c6baef0b",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Fitting causal mechanism of node Gender: 100%|██████████| 5/5 [00:00<00:00, 487.11it/s]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/latex": [
-       "$\\displaystyle 2.1094237467878 \\cdot 10^{-15}$"
-      ],
-      "text/plain": [
-       "2.1094237467877974e-15"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "config = {\n",
     "    \"df\": df_sample,\n",
@@ -968,21 +606,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "84b1c218",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plot_counterfactual_fairness(\n",
     "    df_obs=df_obs_aware,\n",